Tesi sul tema "Personalized prediction"
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1
Fernando, Warnakulasuriya Chandima. "Blood Glucose Prediction Models for Personalized Diabetes Management". Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28179.
Abstract (sommario):
Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.
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Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.
Abstract (sommario):
In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-linear covariate effects. To overcome these, a continuation ratio kernel machine (CRKM) model is proposed both to let the data reveal the underlying model and to capture potential non-linearity effect among predictors, so that the prediction accuracy is maximized. In Chapter 2, we seek to develop a kernel machine (KM) score test that can efficiently identify markers that are predictive of treatment difference. This new approach overcomes the shortcomings of the standard Wald test, which is scale-dependent and only take into account linear effect among predictors. To do this, we propose a model-free score test statistics and implement the KM framework. Simulations and real data applications demonstrated the advantage of our methods over the Wald test. In Chapter 3, based on the procedure proposed in Chapter 2, we further add sparsity assumption on the predictors to take into account the real world problem of sparse signal. We incorporate the generalized higher criticism (GHC) to threshold the signals in a group and maintain a high detecting power. A comprehensive comparison of the procedures in Chapter 2 and Chapter 3 demonstrated the advantages and disadvantages of difference procedures under different scenarios.Biostatistics
3
Reggiani, Francesco. "Development and assessment of bioinformatics methods for personalized medicine". Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424693.
Abstract (sommario):
The human genome is a source of information for researchers that study complex diseases with the perspective of a better understanding of the pathologies and the development of new therapeutic strategies. Starting from the beginning of the current century, a growing number of technologies devoted to DNA sequencing have emerged, generally referred to as Next Generation Sequencing (NGS) technologies. NGS gradually decreased the cost of sequencing a human genome to around US$1000, enabling the use of these technologies for clinical and research purposes, such as Genome-wide association studies (GWAS). GWAS studies have enlightened the presence of disease- associated loci, in particular variants that could be used to evaluate the risk of an individual to develop a disease.
Unfortunately, different sources of errors are able to impair the interpretation and use of NGS data: on the one hand, we have noise related to the process of DNA sequencing and read alignment errors, which could lead to false positive calls or artifacts. On the other hand, variants could be poor predictors for the manifestation of their associated disease. Nowadays the challenge of genomic data interpretation has driven the research towards the development of methods for the analysis and interpretation of genomic variations, eventually predicting the probability of a patient to develop a definite disease. A fair evaluation of these tools is essential to understand the applicability of the presented methods in clinical practice. The Critical Assessment of Genome Interpretation (CAGI) has been developed with the aim of defining the current state of art in terms of methods for predicting the impact of genomic changes at molecular and phenotype levels. CAGI is a community-driven experiment in which different prediction methods, developed by a set of invited groups, are evaluated on a common dataset. Unfortunately, no common guidelines were given to evaluate the tools presented in CAGI experiments, this has made the comparison between different CAGI experiments cumbersome, since different mathematical indexes and scripts have been used to evaluate the involved methods.
My PhD project has been focused on the development of software for the assessment of machine learning methods in regression and multiple phenotype challenges. This tool is based on state of the art assessment principles, derived from literature or previous CAGI experiments. This software is available as an R package and has been used to repeat or perform new assessments on a wide range of CAGI experiments. The knowledge acquired during the development of this project was used to evaluate two CAGI 5 challenges: Pericentriolar Material 1 (PCM1) and Intellectual Disability (ID) panel. The experience I have acquired, through the development of all previously mentioned works, has led the improvement and assessment of a machine learning method. In particular, I have developed a software for the prediction of cholesterol levels, based on genotype data. Eventually I have tested the reliability of this method. This tool was the milestone in a project founded by the Italian Ministry of Health.Il genoma umano è una risorsa ricca di informazioni per i ricercatori che si dedicano allo studio delle patologie complesse. L’obiettivo di questo genere di ricerche è giungere ad una migliore comprensione di queste malattie e quindi sviluppare nuove strategie terapeutiche per la cura dei pazienti affetti. Dall’inizio di questo secolo, un numero crescente di tecnologie per il sequenziamento del DNA sono state sviluppate, sono conosciute come tecnologie “Next Generation Sequencing” (NGS). Le tecnologie NGS hanno gradualmente diminuito il costo del sequenziamento di un genoma umano fino a circa 1000 dollari, ciò ha consentito l’utilizzo di questi strumenti nella pratica clinica e nella ricerca, in particolare negli studi di associazione genome-wide o “Genome-wide association studies” (GWAS). Questi lavori hanno portato alla luce l’associazione di alcune varianti con alcune patologie o caratteri complessi. Queste varianti potrebbero essere utilizzate per valutare il rischio che un individuo sviluppi una particolare patologia. Sfortunatamente diverse sorgenti di errore sono in grado di ostacolare l’uso e l’interpretazione dei dati genomici: da una parte abbiamo il rumore legato al processo di sequenziamento e gli errori di allineamento delle reads. Dall’altra parte gli SNP non sempre possono essere utilizzati in modo affidabile per predire l’insorgenza della malattia a cui sono stati associati. Il Critical Assessment of Genome Interpretation è stato organizzato con l’obiettivo di definire lo stato dell’arte nei metodi che stimano l’effetto di variazioni genetiche a livello molecolare o fenotipico. Negli anni il CAGI ha dato vita a più competizioni in cui diversi gruppi di ricerca hanno testato i loro metodi di predizione su diversi dataset condivisi. L’assenza di linee generali su come condurre la valutazione delle performance dei predittori, ha reso difficile un confronto fra metodi sviluppati in edizioni diverse del CAGI. In questo contesto, il progetto di dottorato si è focalizzato nello sviluppo di un software per la valutazione di metodi di apprendimento automatici basati sulla regressione o la predizione di fenotipi multipli. Questo strumento si fonda su criteri di analisi della performance, derivanti dalla letteratura e da precedenti esperimenti del CAGI. Questo software è stato sviluppato in R ed utilizzato per ripetere o valutare ex novo la qualità dei predittori in un gran numero di esperimenti del CAGI. Le conoscenze acquisite durante lo sviluppo di questo progetto, sono state utilizzate per valutare due competizioni del CAGI 5: la Pericentriolar Material 1 (PCM1) e il Pannello per le Disabilità Intellettive (ID). L’esperienza derivante dal completamento dei lavori precedentemente elencati, ha guidato lo sviluppo e il miglioramento delle prestazioni di un metodo predittivo. In particolare è stato sviluppato un software per la predizione dei livelli di colesterolo, basato su dati genotipici, di cui è stata testata la validità con criteri matematici allo stato dell’arte. Questo strumento è stato la pietra portante di un progetto fondato dal Ministero della Salute Italiano.
4
Bucci, Francesca. "Personalized biomechanical model of a patient with severe hip osteoarthritis for the prediction of pelvic biomechanics". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15879/.
Abstract (sommario):
L’articolazione dell'anca è un'articolazione sinoviale sferica che costituisce la connessione primaria tra gli arti inferiori e lo scheletro della parte superiore del corpo. Durante le attività quotidiane di routine, carichi anormali ripetuti sull'anca possono portare alla danneggiamento della cartilagine articolare e conseguentemente , all’osteoartrite (OA).
L'OA dell'anca è una condizione muscolo-scheletrica cronica e progressiva, il cui trattamento per i pazienti severi è l'artroplastica totale dell'anca (THA). Il centro dell'articolazione dell'anca (HJC) ha grande importanza nell’analisi della biomeccanica dell’anca, così come il suo spostamento, che puo’ essere dovuto a patologie, come OA, o alla chirurgia, THA.
Per valutare la biomeccanica del bacino in questa tesi sono stati implementati un modello muscoloscheletrico (NMS) personalizzato statistical shape e modelli ad elementi finiti (FE) di un paziente con grave OA mono-laterale dell'anca.
Viene discussa l'accuratezza relativamente al modello scalato generico nella predizione delle grandezze biomeccaniche piu’ importanti, durante la deambulazione.
Attraverso i modelli FE, è stato studiato l'effetto di una cattiva stima e/o dello spostamento del centro dell'articolazione dell'anca nelle direzioni antero-posteriore, mediolaterale o infero-superiore per valutare lo stato di sollecitazione della pelvi.
Infine sono presentati i risultati di un approccio multiscala integrato, per valutare le caratteristiche biomeccaniche del suddetto paziente, passando dalla modellazione NMS, all’analisi del modello FE della pelvi, per effettuare un’analisi comparativa dell’arto osteoartritico con il modello dall’arto controlaterale prima dell’intervento e dopo lo stesso
5
Youssfi, Younès. "Exploring Risk Factors and Prediction Models for Sudden Cardiac Death with Machine Learning". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG006.
Abstract (sommario):
La mort subite de l'adulte est définie comme une mort inattendue sans cause extracardiaque évidente, survenant avec un effondrement rapide en présence d'un témoin, ou en l'absence de témoin dans l'heure après le début des symptômes. Son incidence est estimée à 350,000 personnes par an en Europe et 300,000 personnes aux Etats-Unis, ce qui représente 10 à 20% des décès dans les pays industrialisés. Malgré les progrès réalisés dans la prise en charge, le pronostic demeure extrêmement sombre. Moins de 10% des patients sortent vivants de l'hôpital après la survenue d'une mort subite. Les défibrillateurs automatiques implantables offrent une solution thérapeutique efficace chez les patients identifiés à haut risque de mort subite. Leur identification en population générale demeure donc un enjeu de santé publique majeur, avec des résultats jusqu'à présent décevants. Cette thèse propose des outils statistiques pour répondre à ce problème, et améliorer notre compréhension de la mort subite en population générale. Nous analysons les données du Centre d'Expertise de la Mort Subite et les bases médico-administratives de l'Assurance Maladie, pour développer trois travaux principaux :- La première partie de la thèse vise à identifier de nouveaux sous-groupes de mort subite pour améliorer les modèles actuels de stratification du risque, qui reposent essentiellement sur des variables cardiovasculaires. Nous utilisons des modèles d'analyse du langage naturel et de clustering pour construire une nouvelle représentation pertinente de l'historique médical des patients.- La deuxième partie vise à construire un modèle de prédiction de la mort subite, capable de proposer un score de risque personnalisé et explicable pour chaque patient, et d'identifier avec précision les individus à très haut risque en population générale. Nous entraînons pour cela un algorithme de classification supervisée, combiné avec l'algorithme SHapley Additive exPlanations, pour analyser l'ensemble des consommations de soin survenues jusqu'à 5 ans avant l'événement.- La dernière partie de la thèse vise à identifier le niveau optimal d'information à sélectionner dans des bases médico-administratives de grande dimension. Nous proposons un algorithme de sélection de variables bi-niveaux pour des modèles linéaires généralisés, permettant de distinguer les effets de groupe des effets individuels pour chaque variable. Cet algorithme repose sur une approche bayésienne et utilise une méthode de Monte Carlo séquentiel pour estimer la loi a posteriori de sélection des variablesSudden cardiac death (SCD) is defined as a sudden natural death presumed to be of cardiac cause, heralded by abrupt loss of consciousness in the presence of witness, or in the absence of witness occurring within an hour after the onset of symptoms. Despite progress in clinical profiling and interventions, it remains a major public health problem, accounting for 10 to 20% of deaths in industrialised countries, with survival after SCD below 10%. The annual incidence is estimated 350,000 in Europe, and 300,000 in the United States. Efficient treatments for SCD management are available. One of the most effective options is the use of implantable cardioverter defibrillators (ICD). However, identifying the best candidates for ICD implantation remains a difficult challenge, with disappointing results so far. This thesis aims to address this problem, and to provide a better understanding of SCD in the general population, using statistical modeling. We analyze data from the Paris Sudden Death Expertise Center and the French National Healthcare System Database to develop three main works:- The first part of the thesis aims to identify new subgroups of SCD to improve current stratification guidelines, which are mainly based on cardiovascular variables. To this end, we use natural language processing methods and clustering analysis to build a meaningful representation of medical history of patients.- The second part aims to build a prediction model of SCD in order to propose a personalized and explainable risk score for each patient, and accurately identify very-high risk subjects in the general population. To this end, we train a supervised classification algorithm, combined with the SHapley Additive exPlanation method, to analyze all medical events that occurred up to 5 years prior to the event.- The last part of the thesis aims to identify the most relevant information to select in large medical history of patients. We propose a bi-level variable selection algorithm for generalized linear models, in order to identify both individual and group effects from predictors. Our algorithm is based on a Bayesian approach and uses a Sequential Monte Carlo method to estimate the posterior distribution of variables inclusion
6
Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.
Abstract (sommario):
Les effets indésirables médicamenteux (EIM) ont des répercussions considérables tant sur la santé que sur l'économie. De 1,9% à 2,3% des patients hospitalisés en sont victimes, et leur coût a récemment été estimé aux alentours de 400 millions d'euros pour la seule Allemagne. De plus, les EIM sont fréquemment la cause du retrait d'un médicament du marché, conduisant à des pertes pour l'industrie pharmaceutique se chiffrant parfois en millions d'euros.De multiples études suggèrent que des facteurs génétiques jouent un rôle non négligeable dans la réponse des patients à leur traitement. Cette réponse comprend non seulement les effets thérapeutiques attendus, mais aussi les effets secondaires potentiels. C'est un phénomène complexe, et nous nous tournons vers l'apprentissage statistique pour proposer de nouveaux outils permettant de mieux le comprendre.Nous étudions différents problèmes liés à la prédiction de la réponse d'un patient à son traitement à partir de son profil génétique. Pour ce faire, nous nous plaçons dans le cadre de l'apprentissage statistique multitâche, qui consiste à combiner les données disponibles pour plusieurs problèmes liés afin de les résoudre simultanément.Nous proposons un nouveau modèle linéaire de prédiction multitâche qui s'appuie sur des descripteurs des tâches pour sélectionner les variables pertinentes et améliorer les prédictions obtenues par les algorithmes de l'état de l'art. Enfin, nous étudions comment améliorer la stabilité des variables sélectionnées, afin d'obtenir des modèles interprétablesAdverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications
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Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support". Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.
Abstract (sommario):
The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles. This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods. This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support? Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services. The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach. The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support. The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming.
8
Levillain, Hugo. "Prediction and improvement of radioembolization outcome using personalised treatment and dosimetry". Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/320561/3/PhDTM.docx.
Abstract (sommario):
Radioembolization (also called selective internal radiation therapy, SIRT) with yttrium-90 (90Y)-loaded microspheres has been broadly adopted as a locoregional therapy for primary and metastatic liver cancers. Although radioembolization is a well-established therapy, efforts to personalise and refine the planning and administration of therapy are ongoing. The ability to accurately predict, plan and deliver optimal doses to tumour and non-tumour tissues, including final validation of dose distribution, is essential for successful radiotherapy. Determining the true dose absorbed by tissue compartments is the primary way to safely individualise therapy for maximal response while respecting normal tissue tolerances. The overarching objective of this work was to expand our knowledge of dosimetry in 90Y-resin-microsphere radioembolization, with the ultimate goal of improving the clinical outcomes in our patients. Initially we sought to identify the patient- and treatment-related variables that predict radioembolization outcome in patients with intrahepatic cholangiocarcinoma (Chapter 2). Then, as a step toward personalised radioembolization in liver metastases from colorectal cancer patients, we evaluated the relationship between radioembolization real absorbed dose, as determined by 90Y positron emission tomography, and outcome (lesion-based and patient-based) (Chapter 3). In the work described in Chapter 4, we compared predictive (simulated) and post-treatment (real) dosimetry in liver metastases from colorectal cancer patients to pursue radioembolization personalisation. Finally, based on experience accumulated in previous studies and advances reported in the literature, we generated state-of-the-art recommendations to assist practitioners in performing personalised radioembolization with 90Y-resin microspheres in patients with primary and metastatic liver tumours (Chapter 5).Doctorat en Sciences biomédicales et pharmaceutiques (Médecine)
info:eu-repo/semantics/nonPublished
9
Tay, Darwin. "Decision support continuum paradigm for cardiovascular disease : towards personalized predictive models". Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/25032.
Abstract (sommario):
Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for 'personalizing' medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today's world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.
10
Mohammed, Rafiq. "Personalized call center traffic prediction to enhance management solution with reference to call traffic jam mitigation a case study on Telecom New Zealand Ltd. : a dissertation submitted to Auckland University of Technology in partial fulfillment of the requirements for the degree of Master of Computer and Information Sciences (MCIS), 2008 /". Click here to access this resource online, 2008. http://hdl.handle.net/10292/479.
11
Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.
Abstract (sommario):
Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules.
Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.
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Alderdice, Matthew. "Personalised medicine in rectal cancer : understanding and predicting response to neoadjuvant chemoradiotherapy". Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725327.
Abstract (sommario):
Around 12-15% of patients with locally advanced rectal cancer (LARC) undergo a pathologically complete response (Tumour Regression Grade 4 - TRG4) to neoadjuvant chemoradiotherapy; the remainder exhibit a spectrum of tumour regression (TRG1-3). Understanding therapy-related genomic alterations may help us better predict response, progression-free and overall survival, and also identify both novel and repurposed treatment strategies based on the underlying biology of the disease. The Northern Ireland Biobank provided 48 formalin fixed paraffin embedded (FFPE) rectal cancer biopsies and matched resections following neoadjuvant therapy (discovery cohort). These were analysed using high-throughput gene expression microarray, DNA mutational profiling and microsatellite instability profiling. Differential gene expression analysis (analysis of variance) was performed contrasting tumour regression grades in both biopsies and resections to identify predictive and therapy related features. Real time PCR was utilised for microarray validation while immunohistochemistry (IHC) was employed to measure CD56+ cell populations in an independent (validation) cohort (n=150). A NK cell-like gene expression signature was observed following long course chemoradiotherapy in a tumour regression-dependent manner. CD56+ NK cel, populations were measured by IHC and found to be significantly higher in TRG3 patients. Furthermore, it was observed that patients positive for CD56 ceils after therapy had a better overall survival (HR=0.282, 95%C,=0.109-0.729, x2=7.854, p=.OO5). In silico drug selection using QUADrATiC analysis identified clinically relevant therapeutic FDA-approved compounds based upon the NK cell-like signature. We demonstrated that identifying an independently validated predictive signature from biopsies for LARC patients treated with LCPCRT was not possible. However, we identified a novel post-therapeutic NK-like transcription signature in patients responding to neoadjuvant chemoradiotherapy. Furthermore, CD56 positive patients had better overall survival. Therefore, harnessing an NK-like response after therapy may improve outcomes for locally advanced rectal cancer patients.
13
Gilholm, Patricia. "Methods for personalised predictive modelling of developmental milestones for children with disabilities". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212038/1/Patricia%20Gilholm%20Thesis.pdf.
Abstract (sommario):
This thesis developed methods for personalised modelling of developmental milestones for children with disabilities. Using data containing 348 milestone measurements from a small sample of children with a diverse range of disabilities, methods were developed to create a comprehensive personalised developmental profile for each child. These profiles incorporate multiple developmental domains and are designed to be updated in real time so that parents can be provided with feedback as their child develops. The outputs of the methods developed in this thesis will be used to help inform decision-making and assist with personalised intervention planning at the Developing Foundation.
14
Park, Keon-Young. "Predicting patient-to-patient variability in proteolytic activity and breast cancer progression". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53479.
Abstract (sommario):
About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will become invasive over time. Patient variability due to genetic polymorphisms has been investigated, but studies on variability at the level of cellular activity have been extremely limited. An individual’s biochemical milieu of cytokines, growth factors, and other stimuli contain a myriad of cues that pre-condition cells and induce patient variability in response to tumor progression or treatment.
Circulating white blood cells called monocytes respond to these cues and enter tissues to differentiate into monocyte-derived macrophages (MDMs) and osteoclasts that produce cysteine cathepsins, powerful extracellular matrix proteases. Cathepsins have been mechanistically linked to accelerated tumor growth and metastasis. This study aims to elucidate the variability in disease progression among patients by examining the variability of protease production from tissue-remodeling macrophages and osteoclasts. Since most extracellular cues initiate multiple signaling cascades that are interconnected and dynamic, this current study uses a systems biology approach known as cue-signal-response (CSR) paradigm to capture this complexity comprehensively.
The novel and significant finding of this study is that we have identified and predicted donor-to-donor variability in disease modifying cysteine cathepsin activities in macrophages and osteoclasts. This study applied this novel finding to the context of tumor invasion and showed that variability in tumor associated macrophage cathepsin activity and their inhibitor cystatin C level mediates variability in cancer cell invasion.
These findings help to provide a minimally invasive way to identify individuals with particularly high remodeling capabilities. This could be used to give insight into the risk for tumor invasion and develop a personalized therapeutic regime to maximize efficacy and chance of disease free survival.
15
Fu, Yu. "Analyse intégrative de données génomiques et pharmacologiques pour une meilleure prédiction de la réponse aux médicaments anti-cancer". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS560.
Abstract (sommario):
Analyse intégrative de données génomiques et pharmacologiques pour améliorer la prédiction de la réponse aux thérapies cibléesL'utilisation de thérapies ciblées dans le contexte de la médecine personnalisée du cancer a permis d’améliorer le traitement des patients dans différents types de cancer. Cependant, alors que la décision thérapeutique est basée sur une unique altération moléculaire (par exemple une mutation ou un changement du nombre de copies d’un gène), les tumeurs montrent différents degrés de réponse. Dans cette thèse, nous démontrons que la décision thérapeutique basée sur une unique altération n’est pas optimale et nous proposons un modèle mathématique intégrant des données génomiques et pharmacologiques pour identifier de nouveaux biomarqueurs prédictifs de la réponse thérapeutique. Le modèle a été construit à partir de deux bases de données de lignées cellulaires (the Genomics of Drug Sensitivity in Cancer, GDSC and the Cancer Cell Line Encyclopedia, CCLE) et validé avec des données de lignées et des données cliniques. De plus, nous avons également développé une nouvelle méthode pour améliorer la détection des mutations somatiques à partir de données de séquençage d'exomes complets et proposons un nouvel outil, cmDetect, disponible gratuitement pour la communauté scientifiqueIntegrated analysis of genomic and pharmacological data to better predict the response to targeted therapiesThe use of targeted therapies in the context of cancer personalized medicine has shown great improvement of patients’ treatment in different cancer types. However, while the therapeutic decision is based on a single molecular alteration (for example a mutation or a gene copy number change), tumors will show different degrees of response. In this thesis, we demonstrate that a therapeutic decision based on a unique alteration is not optimal and we propose a mathematical model integrating genomic and pharmacological data to identify new single predictive biomarkers as well as combinations of biomarkers of therapy response. The model was trained using two public large-scale cell line data sets (the Genomics of Drug Sensitivity in Cancer, GDSC and the Cancer Cell Line Encyclopedia, CCLE) and validated with cell line and clinical data. Additionally, we also developed a new method for improving the detection of somatic mutations using whole exome sequencing data and propose a new tool, cmDetect, freely available to the scientific community
16
Hwang, Yuan-Chun. "Local and personalised models for prediction, classification and knowledge discovery on real world data modelling problems". Click here to access this resource online, 2009. http://hdl.handle.net/10292/776.
Abstract (sommario):
This thesis presents several novel methods to address some of the real world data modelling issues through the use of local and individualised modelling approaches. A set of real world data modelling issues such as modelling evolving processes, defining unique problem subspaces, identifying and dealing with noise, outliers, missing values, imbalanced data and irrelevant features, are reviewed and their impact on the models are analysed. The thesis has made nine major contributions to information science, includes four generic modelling methods, three real world application systems that apply these methods, a comprehensive review of the real world data modelling problems and a data analysis and modelling software. Four novel methods have been developed and published in the course of this study. They are: (1) DyNFIS – Dynamic Neuro-Fuzzy Inference System, (2) MUFIS – A Fuzzy Inference System That Uses Multiple Types of Fuzzy Rules, (3) Integrated Temporal and Spatial Multi-Model System, (4) Personalised Regression Model. DyNFIS addresses the issue of unique problem subspaces by identifying them through a clustering process, creating a fuzzy inference system based on the clusters and applies supervised learning to update the fuzzy rules, both antecedent and consequent part. This puts strong emphasis on the unique problem subspaces and allows easy to understand rules to be extracted from the model, which adds knowledge to the problem. MUFIS takes DyNFIS a step further by integrating a mixture of different types of fuzzy rules together in a single fuzzy inference system. In many real world problems, some problem subspaces were found to be more suitable for one type of fuzzy rule than others and, therefore, by integrating multiple types of fuzzy rules together, a better prediction can be made. The type of fuzzy rule assigned to each unique problem subspace also provides additional understanding of its characteristics. The Integrated Temporal and Spatial Multi-Model System is a different approach to integrating two contrasting views of the problem for better results. The temporal model uses recent data and the spatial model uses historical data to make the prediction. By combining the two through a dynamic contribution adjustment function, the system is able to provide stable yet accurate prediction on real world data modelling problems that have intermittently changing patterns. The personalised regression model is designed for classification problems. With the understanding that real world data modelling problems often involve noisy or irrelevant variables and the number of input vectors in each class may be highly imbalanced, these issues make the definition of unique problem subspaces less accurate. The proposed method uses a model selection system based on an incremental feature selection method to select the best set of features. A global model is then created based on this set of features and then optimised using training input vectors in the test input vector’s vicinity. This approach focus on the definition of the problem space and put emphasis the test input vector’s residing problem subspace. The novel generic prediction methods listed above have been applied to the following three real world data modelling problems: 1. Renal function evaluation which achieved higher accuracy than all other existing methods while allowing easy to understand rules to be extracted from the model for future studies. 2. Milk volume prediction system for Fonterra achieved a 20% improvement over the method currently used by Fonterra. 3. Prognoses system for pregnancy outcome prediction (SCOPE), achieved a more stable and slightly better accuracy than traditional statistical methods. These solutions constitute a contribution to the area of applied information science. In addition to the above contributions, a data analysis software package, NeuCom, was primarily developed by the author prior and during the PhD study to facilitate some of the standard experiments and analysis on various case studies. This is a full featured data analysis and modelling software that is freely available for non-commercial purposes (see Appendix A for more details). In summary, many real world problems consist of many smaller problems. It was found beneficial to acknowledge the existence of these sub-problems and address them through the use of local or personalised models. The rules extracted from the local models also brought about the availability of new knowledge for the researchers and allowed more in-depth study of the sub-problems to be carried out in future research.
17
Bragazzi, Nicola Luigi [Verfasser], e Norbert [Akademischer Betreuer] Hampp. "Nanogenomics and Nanoproteomics Enabling Personalized, Predictive and Preventive Medicine / Nicola Luigi Bragazzi. Betreuer: Norbert Hampp". Marburg : Philipps-Universität Marburg, 2014. http://d-nb.info/1051935334/34.
18
IANZA, ANNA. "VALIDATION OF PREDICTIVE AND PROGNOSTIC BIOMARKERS AS A GUIDE FOR A PERSONALIZED APPROACH IN SOLID TUMOURS". Doctoral thesis, Università degli Studi di Trieste, 2020. http://hdl.handle.net/11368/2973745.
Abstract (sommario):
Breast cancer (BC), Colorectal Cancer (CRC) and Non-Small Cell Lung Cancer (NSCLC) are among the most commonly diagnosed solid tumors, and occupy the first places in the mortality rankings. Compared to an old fashioned one-size-fits-all approach, precision medicine offers the possibility to accurately choose the most appropriate therapeutic strategy, that fits the patients not only from the clinical (age, comorbidities) but also from a molecular point of view. A genetic and biological understanding of the tumor, integrated with a weighted analysis of results can help the clinician in designing a therapeutic pathway that, ideally from the start, gives the patients the best response rates. The aim of my research is to evaluate the markers that have the greatest impact on the prediction of therapy response.
Mutational analysis revolutionized the NSCLC treatment paradigm and, consequently, improved the prognosis. EGFR mutated patients benefit from target therapy with tyrosine kinase inhibitors. A fluid and longitudinal monitoring of mutational status is becoming a key factor in disease management. Firstly we extracted circulating free DNA (cfDNA) from the plasma of 30 patients with EGFR-mutated NSCLC and assessed mutational status with real-time PCR. We then monitored such mutation during target therapy in 19 patients. The liquid biopsy had a sensitivity of 60% in confirming the tissue mutation. Patients whose EGFR mutation was not detectable on plasma had a longer Progression free survival (PFS) and Overall survival (OS). Next step will be assessing if cfDNA analysis allows early detection of resistance mutation such as T790M.
Next part of my research focused on luminal BC, working partially retrospectively on data from a phase III study of 90 ER-positive, HER2 negative locally advanced breast cancer patients that were randomly assigned 1:1 to receive Let 2,5 mg daily and metronomic oral Cyc 50 mg daily with (arm B; n=45) or without (arm A, n=45) sorafenib 400 mg/bid daily for six months as neoadjuvant treatment. The predictive role of Ki67, SUV variations and metabolic response and its changes with regards to clinical response and survival was analyzed.
The serum of 32 patients was analyzed via Luminex Multiplex Panel technology. 38 analytes (cytokines and growth factors) were simultaneously measured according to arm of treatment and time of sample collection (before and after treatment). Patients were divided into groups according to response to therapy (RECIST). Then we investigated a possible link between chemotherapy-induced RNA disruption and survival/progression. Analysis were performed on 40 biopsies taken at baseline and 15 days after the beginning of the neoadjuvant therapy. The RNA for each sample or subdivided sample was then assessed using the RNA Disruption Assay. The maximum RNA disruption Index (RDI) value for each patient at day 15 was used for all analyses. Finally, We investigated the discordance of mutational status between primary and metastatic site in colorectal cancer.Patients with metastatic CRC who underwent surgery of both primary and metastasis were retrospectively evaluated, and mutational status assessment of K-RAS, N-RAS, BRAF and PIK3CA was performed on 21 patients. Median DFS was 20.5 months (95% CI 9.9-29.6) in patients with concordance in mutational status versus 10.4 months (95% CI 6.1-not reached) in patients with discordance (p=0.01) and median OS was 35.9 months (95% CI 26.3-not reached) in patients with concordance in mutational status 25.6 months (95% CI 6.6-not reached) in patients with discordance (p=0.038).
In conclusion discordance seems related to clinical outcome. Overall my results show that new strategies and technologies allow the researchers and the clinicians to strive for a better and more complete understanding of solid tumors complex evolution, an integrated and focused approach to the early disease could become the future of disease management.
19
Tsur, Neta [Verfasser], e Markus [Akademischer Betreuer] Morrison. "Predicting response to immunotherapy in metastatic melanoma by a personalized mathematical model / Neta Tsur ; Betreuer: Markus Morrison". Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2020. http://d-nb.info/1215574142/34.
20
Biasci, Daniele. "Predicting prognosis in Crohn's disease". Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270034.
21
Gil, Ortega Joan. "Improved and efficient therapy of acromegaly by implementation of a personalized and predictive algorithm including molecular and clinical information". Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671121.
Abstract (sommario):
El tractament farmacològic actual de l’acromegàlia està basat en el mètode de prova i error. En aquesta malaltia, un control bioquímic ràpid és decisiu per evitar comorbiditats i reduir la mortalitat. Afortunadament, avui en dia tenim diversos tractaments farmacològics amb els lligands del receptor de la somatostatina (LRS) de primera generació com a primera línia farmacològica. Tanmateix més del 50% dels pacients no aconsegueixen controlar els nivells hormonals amb els LRS la qual cosa pot arribar a endarrerir el control bioquímic de la malaltia durant mesos o fins i tot més d’un any. El principal objectiu d’aquesta tesis és l’avaluació de la potencial utilitat dels diferents marcadors de resposta a LRS i la conseqüent elaboració d’un nou algoritme de tractament amb aquests marcadors. Fent ús de diversos nodes de la cohort REMAH arreu d’Espanya, vam obtenir 100 mostres tumorals d’acromegàlia en les quals vam realitzar anàlisis moleculars. A més a més, vam caracteritzar la resposta a LRS en la majoria dels casos i les dades clíniques associades a aquests pacients. Amb tot això vam ser capaços de validar biomarcadors prèviament reportats (SSTR2, Ki-67, E-cadherin i RORC), descriure l’associació entre el fenomen de transició epiteli-mesènquima i la resposta a LRS en aquests tumors productors d’hormona del creixement, caracteritzar molecularment la millora de l’efecte dels LRS després de cirurgia parcial en tumors grans i invasius, i finalment, definir algoritmes de tractament personalitzats en funció de l’expressió de diversos gens i situacions clíniques. Concloem aquest estudi doncs proposant nous algoritmes de tractament basats en la medicina predictiva i personalitzada per a nous casos d’acromegàlia utilitzant tècniques de quantificació del RNA o immunohistoquímica per tal superar l’estratègia de tractament de prova i error.El tratamiento farmacológico actual de la acromegalia está basado en el método de prueba y error. En esta enfermedad, un control bioquímico rápido es decisivo para evitar comorbilidades y reducir la mortalidad. Afortunadamente, hoy en día tenemos varios tratamientos farmacológicos con los ligandos del receptor de la somatostatina (LRS) de primera generación como primera línea farmacológica. Aun así más del 50% de los pacientes no consiguen controlar los niveles hormonales con los LRS lo cual puede llegar a atrasar el control bioquímico de la enfermedad durante meses o incluso más de un año. El principal objetivo de esta tesis es la evaluación de la potencial utilidad de los diferentes marcadores de respuesta a LRS y la consecuente elaboración de un nuevo algoritmo de tratamiento con estos marcadores. Gracias a varios nodos de la cohorte REMAH en todo España, obtuvimos 100 muestras tumorales de acromegalia en las cuales realizamos análisis moleculares. Además, caracterizamos la respuesta a LRS en la mayoría de los casos y los datos clínicos asociados a estos pacientes. Con todo esto fuimos capaces de validar biomarcadores previamente reportados (SSTR2, Ki-67, E-cadherin y RORC), describir la asociación entre el fenómeno de transición epitelio-mesénquima y la respuesta a LRS en estos tumores productores de hormona del crecimiento, caracterizar molecularmente la mejora del efecto de los LRS después de cirugía parcial en tumores grandes e invasivos, y finalmente, definir algoritmos de tratamiento personalizados en función de la expresión de varios genes y situaciones clínicas. Concluimos este estudio pues proponiendo nuevos algoritmos de tratamiento basados en la medicina predictiva y personalizada para nuevos casos de acromegalia utilizando técnicas de cuantificación del RNA o immunohistoquímica con tal de superar la estrategia de tratamiento de prueba y error.
Actual pharmacologic treatment in acromegaly is currently based upon assay-error strategy. The prompt biochemical control of the disease is essential to reduces comorbidities and mortality. Fortunately, several drugs have been developed over the years to treat acromegaly being first generation somatostatin receptor ligands (SRLs), the first-line treatment. However, up to 50% of patients do not respond adequately to SRLs, which delays biochemical control for months or even a year. The main objective of this thesis was to evaluate the potential usefulness of different molecular markers as predictors of response to SRLs and elaborate a new treatment algorithm accordingly. We taught advantage of the REMAH cohort of several nodes in Spain to collect 100 acromegaly samples and performed molecular analysis. We measured molecular expression by RT-qPCR, measured protein by IHC and; quantified CpG methylation and evaluated mutations by sanger sequencing. Furthermore, we were able to stratify the SRLs respond in the majority of the cases and collected clinical associated data too. Taking all that into account, we have been able to validate reported biomarkers (SSTR2, Ki-67, E-cadherin and RORC) associated to SRLs response, describe the association of the epithelial-mesenchymal transition and SRLs in somatotropinomas, molecularly characterize the SRLs improvement after tumor debulking in large GH-producing tumors and define treatment algorithm based on molecular expression through data mining approaches. We conclude presenting treatment algorithms for new diagnosed acromegaly patients that will benefit from personalized medicine using IHC or more complex RNA quantification approaches to overcome the assay-error strategy in acromegaly treatment.
22
Ahmadi, Matthew. "Application of machine learning approaches for activity recognition and energy expenditure prediction in free living children and adolescents". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/206178/1/Matthew%20Nguyen_Ahmadi_Thesis.pdf.
Abstract (sommario):
This thesis was the first to comprehensively evaluate the application of machine learning approaches for physical activity assessment under real world conditions among children with unique movement patterns. This included preschool age children and children with cerebral palsy. Collectively, the findings from this thesis conclude activity classification models trained on laboratory-based data fail to generalise to a real-world environment and models trained on free-living data have superior accuracy. In contrast, energy expenditure prediction models trained on laboratory-based data generalise to real world environments with no further improvements attained when trained on free-living data.
23
Salehe, Bajuna Rashid. "Predictive tools for the study of variations in ADP platelet responses : implications for personalised CVD risk and prevention strategies". Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/74255/.
Abstract (sommario):
The major aim of this project is to develop novel computational approaches for rapid identification of key omic variations, particularly SNPs that are likely to be associated with the variability of the ADP (Adenosine diphosphate) activated platelet responses. The ADP platelet response was chosen as a model system due to its distinct role during the platelet amplification and aggregation, and it is the main therapeutic target for cardiovascular disease (CVD) antiplatelet drug treatments. Based on recent studies, CVD is currently the second lethal noncommunicable disease after cancer in both developed and developing countries. Interindividual variability of the ADP platelet responses was previously reported in genetic association studies, and susceptible SNPs were identified. However, most of the standard biostatistical methods that were previously employed were found to be suboptimal, and it is assumed that other crucial SNPs might have been potentially missed. In genetics, this phenomenon is known as ‘missing heritability’ problem. Therefore, to address this issue, this study aims to employ alternative computational approaches in an integrated manner in order to identify previously unidentified key SNPs, which may underlie the ADP platelet responses variability. Additionally, the project aims to develop predictive approaches to unveil the molecular mechanisms of the identified key SNPs, which are likely to underpin the interindividual variability in the ADP platelet responses and aggregation. The molecular mechanisms underpinning these SNPs, or ‘omic variations are rarely addressed in standard genetic mapping or association studies. This may be due to the experimental hurdles related to the costs and labour that are required in pursuing such undertakings, hence our predictive approach seeks to address such inefficiencies in closing these knowledge gaps. Moreover, the project culminates in the development of a method for predicting an individuals’ ADP platelet response levels with a focus on determining the extreme cases, i.e., individuals showing high and low responses to ADP platelet activation. Predicting ADP responses levels might be suitable for determining which allelic features will contribute most to the extreme ADP platelet responses. This understanding may be useful for suggesting new drug targets or individualised treatments in the targeted CVD therapeutics or personalised medical settings for the next generation of medical practice.
24
Alyamani, Nayef A. "The impact of cancer physicians' and patients' attitudes on personalised prescription of novel targeted anticancer drugs using predictive biomarkers". Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211204.
Abstract (sommario):
Background: The use of novel targeted anticancer drugs in clinical practice is rapidly increasing. As the use of these drugs increases, so does the need to develop biomarkers to optimise the drugs' clinical and cost effectiveness. The attitudes and views of all stakeholders regarding the optimal use of predictive biomarkers in guiding personalised medicine are crucial for identifying acceptable criteria of predictive biomarker tests to guide future biomarker development. To gain insight into these views, we aimed to develop and validate a survey tool that would aid in assessing attitudes of cancer physicians and patients regarding the utilisation of biomarkers in tailoring treatment according to individual patient needs. Methods: Two questionnaires (one for oncologists and one for patients) based on emerging clinical data about novel targeted anticancer agents were designed to capture information about acceptable sensitivity, specificity, invasiveness and cost of a predictive biomarker test. A hypothetical scenario was provided that described a newly developed, targeted anticancer drug that was found to be more beneficial to certain patient subgroups identified through a predictive biomarker test. Questions in the patient survey were based on the results of the oncologist survey. Results: The response rates to these surveys were 20% (n=67) for the oncologists and 59% (n=100) for the patients. The oncologists' attitudes regarding the predictive biomarker test's false negative (FN) and false positive (FP) rates varied with the level of health outcome generated by the hypothetical drug. The acceptable FN rate for predictive biomarker test results detected in the current study was similar to many current predictive biomarkers, but the FP rate considered acceptable was much lower. The majority of the patients (90%) accepted the median acceptable FN rate of 10% reported by the oncologists. A significant minority of the oncologists (27%) refused a tumour biopsy (in addition to the diagnostic biopsy) to collect samples for the purpose of predictive biomarker testing. A much higher percentage of patients (68%) refused a biopsy under such conditions. Interestingly, our data also suggest that oncologists' expectations for the outcome of therapy have changed little in recent years, while patients' expectations have increased dramatically. Conclusions: Our data suggest that oncologists and patients agree that a FN rate of 10% is acceptable. However, based on the oncologists' responses, the FP rates associated with current predictive biomarkers are far from ideal. This may reflect oncologists' pragmatic approach in the absence of alternative choices for predictive biomarker tests. However, it also suggests that future biomarker development and implementation must focus on decreasing the FP rate without increasing the FN rate. Recent results demonstrating molecular heterogeneity in tumours suggest that, considering our data on acceptable test accuracy, serial/repeated tumour biopsies may be required. However, based on the attitudes of physicians and patients reported here, such ‘biopsy-driven' clinical trials may not be acceptable, and, without further investigation or education, this may be a barrier to the successful implementation of such potentially valuable investigational strategies. These results should certainly be taken into account when planning biopsy-dependent trials, and emphasises the importance of pursuing non-invasive biomarker assays. We believe that the survey questionnaires originating from the current study are valid tools for assessing stakeholders' attitudes and views about the appropriate application of predictive biomarkers in personalised medicine.
25
Wang, Hao. "Screening multi-omics biomarkers for suboptimal health status". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2431.
Abstract (sommario):
Background
Suboptimal health status (SHS) is an intermediate health status between ideal health and diseases. It is characterized by chronic fatigue, perception of health complaints and a cluster of physical symptoms lasting for more than three months. SHS is recognised as a subclinical, reversible stage of chronic diseases.
Objectives
Study I. To investigate the prevalence of SHS in a cross-sectional study. Study II. To screen transcriptomic biomarkers for SHS in a case-control study. Study III. To screen metabolomics biomarkers for SHS in a case-control study.
Materials and Methods
Study I. A cross-sectional study was conducted from September 2017 to November 2017. SHS questionnaire-25 was used to assess the SHS levels of the participants.
Study II. The RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls.
Study III. The liquid chromatography-mass spectrometry (LC-MS)- based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls.
Result
In Study I, a total of 4839 Chinese university students enrolled in this study. The prevalence of SHS was 8.39%, with the prevalence of 6.57% among males and 9.60% among females. The multivariate logistic regression results showed that SHS was significantly associated with age (Odd ratio (OR) = 1.193, P = 0.019), female (OR = 1.437, P = 0.002), sleep duration (OR = 0.609, P < 0.001), insomnia symptoms (OR = 1.238, P < 0.001), anxiety symptoms (OR = 1.025, P = 0.019), and depression symptoms (OR = 1.082, P < 0.001).
In study II, transcriptome analysis identified a total of 46 differentially expressed genes, in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette transporter and neurodegeneration. A combination of transcripts can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3%, a specificity of 90.0%, and an area under the receiver operating characteristic curve (AUC) of 0.938.
In study III, metabolomics analysis identified a total of 24 significantly altered metabolites as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an AUC of 0.977.
Conclusion SHS is prevalent in Chinese university students. Older age, female, insomnia, depression, and anxiety symptoms are risk factors for SHS, while longer sleep duration is a protective factor for SHS. Blood transcripts and metabolites are valuable biomarkers for SHS identification. These findings suggest the potential utility of SHS-related transcriptomic and metabolomic biomarkers for the Predictive, Preventive, and Personalized Medicine (PPPM) of chronic diseases.
26
Kim, Jin Hee. "Functional genomics of cardiovascular disease risk". Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51769.
Abstract (sommario):
Understanding variability of heath status is highly likely to be an important component of personalized medicine to predict health status of individuals and to promote personal health. Evidences of Genome Wide Association Study and gene expression study indicating that genetic factors affect the risk susceptibility of individuals have suggested adding genetic factors as a component of health status measurements. In order to validate or to predict health risk status with collected personal data such as clinical measurements or genomic data, it is important to have a well-established profile of diseases.
The primary effort of this work was to find genomic evidence relevant to coronary artery disease. Two major methods of genomic analysis, gene expression profiling and GWAS on gene expression, were performed to dissect transcriptional and genotypic fingerprints of coronary artery disease. Blood-informative transcriptional Axes that can be described by 10 covariating transcripts per each Axis were utilized as a crucial measure of gene expression analysis.
This study of the relationship between gene expression variation and various measurements of coronary artery disease delivered compelling results showing strong association between two transcriptional Axes and incident of myocardial infarction. 244 transcripts closely correlated with death by cardiovascular disease related events were also showing clear association with those two transcriptional Axes. These results suggest potential transcripts for use in risk prediction for the advent of myocardial infarction and cardiac death.
27
Hoogendoorn, Corné. "A statistical dynamic cardiac atlas for the virtual physiological human: construction and application". Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/132632.
Abstract (sommario):
This thesis is centered on the construction of a cardiac atlas to serve as a common reference frame in the Virtual Physiological Human (VPH). The construction covers the entire construction pipeline, starting from a set of 3D+t multislice computed tomography images, then performing a spatial normalization of these images, segmentation of the synthesized mean image, multi-structure meshing, and finally mapping of the mesh back to the population of images. In addition, two applications are presented in this thesis.
First, the atlas is used to frame a spatio-temporal model of cardiac morphology which models the variability along both 'axes' simultaneously. Such a unified approach should be preferable over existing methods, which decouple the two sources of variation and then model them separately, in isolation.
Second, the proposed atlas is applied to develop an acceleration technique for performing personalized simulation of cardiac electrophysiology (EP). The prior knowledge encapsulated in our atlas is used, in conjunction with a numerical solver of cardiac EP, to build a statistical model linking cardiac morphology with the steady states of myocardial cell models that pre condition detailed cardiac EP simulations. This application puts the proposed dynamic cardiac atlas in the context of VPH-related simulations, of which the computational costs are currently greatly in excess of what is acceptable for their adoption in current clinical practice.Esta tesis está centrada en la construcción de un atlas cardiaco, para servir como marco común de referencia en el Virtual Physiological Human (VPH). La construcción consiste en la trayectoria completa, empezando con un conjunto de imágenes 3D+t de tomografía computacional multi-corte, y entonces hacer una normalización espacial de las imágenes, segmentación de la imagen promedio sintetizada, un mallado multi-estructura, y finalmente la transformación de la malla a la población de imágenes. Adicionalmente, la tesis presenta dos aplicaciones del atlas. Primero, el atlas se usa para enmarcar un modelo espacio-temporal de la morfología cardiaca que modela la variación a lo largo de ambos 'ejes' simultáneamente. Tal propuesta debe ser preferible sobre otros m\'etodos existentes, los cuales desacoplan las dos fuentes de variación para modelarlas separadamente, en isolación. Segundo, el atlas está aplicado al desarrollo de una técnica de aceleración para simulaciones personalizadas de electrofisiología (EF) cardiaca. El conocimiento previo encapsulado en nuestro atlas se usa, en conjunto con un solver de EF cardiaca, para construir un modelo estadístico conectando morfología cardiaca con los steady states de modelos celulares del miocardio que precondicionan a simulaciones detalladas de EF cardiaca. Esta aplicación posiciona el propuesto atlas dinámico cardiaco en el contexto de simulaciones relacionadas al VPH, cuyo costo computacional actual está en gran exceso de lo aceptable para su adopción en la práctica clínica de hoy en día.
28
Oltra-Cucarella, Javier. "Revisión de los criterios diagnósticos del deterioro cognitivo leve: nuevos marcadores predictivos de la enfermedad de Alzheimer". Doctoral thesis, Universidad de Alicante, 2018. http://hdl.handle.net/10045/103153.
Abstract (sommario):
La presente tesis doctoral se centra en la investigación sobre los criterios diagnósticos del Deterioro Cognitivo Leve y el análisis de nuevos marcadores predictivos de la Enfermedad de Alzheimer. Este trabajo consta de diez capítulos articulados en los siguientes apartados: a) introducción y objetivos, b) estudios empíricos publicados, c) estudios empíricos en revisión, d) discusión general, limitaciones, implicaciones clínicas y perspectivas de futuro, y e) conclusiones. Los estudios empíricos que conforman esta tesis doctoral se han centrado en dos colectivos de interés: personas con Deterioro Cognitivo Leve (DCL) y en personas con Enfermedad de Alzheimer (EA). Los estudios realizados hasta la fecha acerca de la eficacia de las intervenciones farmacológicas y no farmacológicas en personas con EA no han mostrado un impacto significativo en la independencia y funcionalidad en las actividades de la vida diaria de las personas con EA. Por este motivo, en los últimos años se ha incrementado el interés por identificar de manera precoz a las personas con un alto riesgo de progresión a EA, con el objetivo de proporcionar intervenciones centradas en la prevención de la enfermedad en lugar de proporcionar tratamientos sintomáticos. El DCL como categoría diagnóstica utilizada para identificar a personas con alteraciones cognitivas superiores a lo esperable por edad, que mantienen su independencia en las actividades de la vida diaria, ha mostrado utilidad para identificar a las personas con un riesgo elevado de desarrollar EA. Sin embargo, el DCL no está exento de limitaciones para discriminar a personas sanas de personas con alteración cognitiva, ni tampoco para identificar el tipo de variables más eficaces para estimar el riesgo de progresión a EA. La presente tesis doctoral se centra en el estudio de la necesidad y la utilidad del DCL como entidad diagnóstica para identificar a las personas con mayor riesgo de progresión a EA. Para ello se han determinado cuatro objetivos principales con cinco estudios empíricos. Los principales resultados de cada uno de los estudios aparecen recogidos en los capítulos 3-7. En las dos últimas secciones se discuten los hallazgos más relevantes obtenidos en cada uno de los estudios empíricos, sus limitaciones, sus implicaciones clínicas y las perspectivas de futuro para las neurociencias en general y la neuropsicología en particular. En el primer estudio examinamos la eficacia de las diferentes intervenciones cognitivas para personas con EA. En este estudio, en forma de meta-análisis jerárquico, se comparó la eficacia de programas de estimulación cognitiva, entrenamiento cognitivo y rehabilitación cognitiva con grupos placebo en variables relacionadas con la cognición y la funcionalidad en actividades de la vida diaria. En el estudio se controló el efecto de variables que pueden afectar significativamente al tamaño del efecto como la edad, el sexo, el nivel educativo, el riesgo de sesgo, el tamaño de la muestra, la duración de la intervención, la proporción de mortalidad de la muestra o la gravedad de la enfermedad, Los resultados mostraron que los efectos de la estimulación cognitiva y el entrenamiento cognitivo son nulos o irrelevantes en variables cognitivas y funcionales cuando se comparan con grupos placebo. Sin embargo, aunque la rehabilitación cognitiva tampoco mostró efectos significativos en tareas cognitivas estándar, sí mostró efectos significativos relevantes en las tareas entrenadas. El segundo estudio, centrado en población con Deterioro Cognitivo Leve, analizó mediante meta-regresión jerárquica el riesgo de progresión a Enfermedad de Alzheimer en los cuatro subtipos de DCL: amnésico vs. no-amnésico y de dominio único vs. multidominio. En este estudio se controló el efecto de variables que pueden afectar significativamente el tamaño del efecto como los tamaños de las muestras, la fuente de estudio (clínicas vs. comunidad), el formato de diagnóstico (algoritmo vs. comité de expertos), el diagnóstico ciego de EA o el número de variables incluidas en la evaluación neuropsicológica. Nuestros resultados mostraron que el DCL amnésico presenta un mayor riesgo de progresión a EA que el DCL no-amnésico, sin diferencias entre el DCL amnésico de dominio único y el multidominio, así como la influencia de otras variables en la estimación del riesgo. En el tercer estudio se analizó la variabilidad normal en personas cognitivamente sanas evaluadas mediante una batería neuropsicológica. Los resultados mostraron que la mayoría de las personas sanas obtienen hasta dos puntuaciones iguales o mayores que 1.5 desviaciones estándar por debajo de la media cuando se aplican nueve tests que evalúan diferentes habilidades cognitivas. Al utilizar tres puntuaciones bajas como criterio diagnóstico de DCL se observó que la identificación de personas con un mayor riesgo de EA fue superior en comparación con los criterios originales y otros criterios estándar para el diagnóstico de DCL. Los dos últimos estudios analizaron la utilidad de las alteraciones de memoria para identificar a las personas con mayor riesgo de progresión a EA. En concreto, el cuarto estudio analizó los efectos de la práctica en una tarea de memoria verbal en personas cognitivamente sanas, y los resultados se utilizaron para identificar a las personas con DCL que no presentaban efectos de la práctica en la mima tarea. Los efectos de la práctica demostraron ser más eficaces que los datos genéticos (APOE) y de metabolismo cerebral para identificar a las personas con DCL y un mayor riesgo de progresión a EA. El último estudio analizó la utilidad de las alteraciones en tareas de memoria verbal y de memoria visual para identificar a las personas con DCL y un mayor riesgo de progresión a EA. Los resultados mostraron que las personas con alteraciones de memoria presentan más riesgo de EA que las personas sin alteración cognitiva con independencia del tipo de memoria alterado. Asimismo, las personas con alteraciones verbales o alteraciones visuales presentan un riesgo similar, mientras que las personas con alteraciones verbales y visuales presentan el mayor riesgo de progresión a EA. En el capítulo 9 se discuten los principales hallazgos obtenidos en cada estudio empírico junto con las limitaciones, las implicaciones clínicas y las perspectivas de futuro, y se sugieren nuevas líneas de investigación que aumenten el conocimiento científico sobre el riesgo de progresión a EA en personas con DCL. Las conclusiones más relevantes que se pueden extraer de los estudios que conforman esta tesis doctoral son que es necesario incluir la variabilidad normal en el rendimiento en tareas cognitivas para identificar con mayor precisión a las personas con el riesgo más alto de progresión a EA, para lo cual es necesario aplicar conjuntamente tareas de memoria verbal y tareas de memoria visual en la evaluación neuropsicológica. Asimismo, es necesario desarrollar nuevos programas de intervención para personas con EA centrados en sus necesidades y en actividades relevantes para la independencia en las actividades de la vida diaria de estas personas, así como utilizar medidas de evaluación relacionadas con las tareas entrenadas que identifiquen con mayor precisión los efectos de las intervenciones cognitivas.
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Gallart, Masià Salvador. "Predicción de los trastornos de la personalidad del Eje II del DSM-IV-TR a partir de diferentes modelos dimensionales: Costa y McCrae, Zuckerman y Cloninger". Doctoral thesis, Universitat de Lleida, 2015. http://hdl.handle.net/10803/306603.
Abstract (sommario):
Aquest estudi pretén comprovar l’existència o no de diferències estructurals entre la personalitat normal i patològica. D’altra banda, interessa veure la capacitat predictiva dels tests de personalitat normal respecte als trastorns de la personalitat segons els criteris del DSM-IV-TR, en població sana i clínica. S'utilitzen tres instruments psicomètrics (NEO-PI-R, ZKPQ-50-CC i TCI-R), pertanyents als models dimensionals de personalitat de Costa i McCrae (Cinc Grans Factors), Zuckerman (Cinc Alternatius) i Cloninger (Psicobiològic de Temperament i Caràcter), respectivament. Els resultats confirmen que l’estructura de la personalitat no varia en població sana i clínica. Les diferències són quantitatives i no qualitatives, i els trets es desenvolupen en diversos graus: des de la personalitat normal fins a la patològica. Es repliquen les prediccions a partir dels tres qüestionaris emprats respecte als trastorns de la personalitat, tal i com evidencien altres estudis previs realitzats en diferents contexts transculturals.Este estudio pretende comprobar la existencia o no de diferencias estructurales entre la personalidad normal y patológica. Por otro lado, interesa ver la capacidad predictiva de los tests de personalidad normal respecto a los trastornos de la personalidad según los criterios del DSM-IV-TR, en población sana y clínica. Se utilizan tres instrumentos psicométricos (NEO-PI-R, ZKPQ-50-CC y TCI-R), pertenecientes a los modelos dimensionales de personalidad de Costa y McCrae (Cinco Grandes Factores), Zuckerman (Cinco Alternativos) y Cloninger (Psicobiológico de Temperamento y Carácter), respectivamente. Los resultados confirman que la estructura de la personalidad no varía en población sana y clínica. Las diferencias son cuantitativas y no cualitativas, y los rasgos se desarrollan en diversos grados: desde la personalidad normal hasta la patológica. Se replican las predicciones a partir de los tres cuestionarios utilizados respecto a los trastornos de la personalidad, tal y como evidencian otros estudios previos realizados en diferentes contextos transculturales.
The aim of the current study is to check whether there are structural differences between normal and psychopathological personality. On the other hand, we are interested to see what is the predictive capacity of normal personality tests in relation to personality disorders according to the DSM-IV-TR criteria, in healthy and patient population. Three psychometric instruments have been used (NEO-PI-R, ZKPQ-50-CC and TCI-R), corresponding to the dimensional personality models of Costa and McCrae’s Big Five, Zuckerman’s Alternative Five Factor Model and Cloninger’s Psychobiological model of Temperament and Character, respectively. Results confirm that the structure of personality do not vary in healthy and patient population. Differences are quantitative but not qualitative, and traits are developed in different degrees: from normal personality to psychopatological personality. Predictions are replicated through the three questionnaires with respect to personality disorders, as evidenced by other previous research carried out in different crosscultural contexts.
30
Alcenat, Stéphane. "Assurance maladie et tests génétiques". Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCB002.
Abstract (sommario):
Cette thèse apporte trois contributions majeures. Le premier chapitre, un article paru dans la Revue Française d’Économie n°2/vol XXXIV, propose une revue de la littérature sur les implications de la régulation des tests génétiques de prédisposition sur le marché de l'assurance santé. Nous montrons que les formes actuelles de régulation réalisent un arbitrage entre la maximisation du bien-être social ex ante et l’incitation à la prévention. Cet arbitrage est conditionné par la façon dont l’acquisition de l’information affecte les comportements de prévention et de révélation des agents, la discrimination des risques par les assureurs ainsi que la nature des contrats. Le deuxième chapitre étudie théoriquement l’impact de la reclassification sur la prévention, la décision de faire le test ainsi que sur le bien-être social dans la réglementation d’obligation de divulgation. En particulier, nous montrons qu’en fonction du coût de l’effort de prévention la valeur individuelle de l’information génétique avec reclassification peut être plus élevée que sans reclassification. De plus, nous montrons comment les préférences temporelles affectent la valeur individuelle de l’information génétique. D’après nos résultats, le bien-être social est strictement plus élevé sans reclassification qu'avec reclassification. Le dernier chapitre étudie et caractérise des contrats incitatifs pouvant être mis en œuvre pour développer la médecine personnalisée avec des traitements très efficaces, dans un contexte d'aléa moral sur l’effort fourni par la firme pour améliorer l'efficacité du médicament. Nous considérons un modèle dans lequel l'autorité de santé a trois possibilités. Il peut appliquer le même traitement (standard ou nouveau traitement) à l'ensemble de la population ou mettre en œuvre la médecine personnalisée, c'est-à-dire utiliser les informations génétiques pour proposer le traitement le plus adapté à chaque patient. Nous caractérisons d'abord le contrat de remboursement de médicaments d'une entreprise produisant un nouveau traitement avec un test génétique compagnon lorsque l'entreprise peut entreprendre un effort pour améliorer la qualité des médicaments. Ensuite, nous déterminons les conditions sous lesquelles la médecine personnalisée doit être mise en œuvre lorsque cet effort est observable et quand il ne l'est pas. Enfin, nous montrons comment la non observabilité de l'effort affecte la décision de l'autorité sanitaire de mettre en œuvre la médecine personnalisée avec des traitements hautement efficacesThis thesis includes three main contributions. The first chapter, an article published in 2019 in the “Revue Française d’Économie n°2/vol XXXIV”, provides a literature review on the implications of genetic testing regulations on the health insurance market. We show that the choice of a regulation results from a trade-off between the maximization of ex-ante social welfare and incentive to undertake prevention actions. Indeed, this trade-off depends on the way information acquisition impacts prevention and revelation behaviors of agents, as well as of its impact on insurance contract. The second chapter studies theoretically how reclassification impacts testing and prevention decision as well as social welfare in the Disclosure Duty regulation. In particular, we show that the incentives of agents to take genetic with reclassification can be higher than without reclassification according to the effort cost. In addition, we show how time preferences affect the incentive to take genetic testing. Finally, we show that the social welfare is strictly higher without reclassification than with reclassification. The last chapter studies and characterizes contracts that can be implemented to develop personalized medicine with highly effective treatment in context of moral hazard about firm effort to improve drug efficacy. It also studies how the non-observability of effort impacts the decision of a health authority to implement personalized medicine with highly effective treatments. We consider a model in which the health authority has three possibilities. It can apply either the same treatment (a standard or a new treatment) to the whole population or implement personalized medicine, i.e., use genetic information to offer the most suitable treatment to each patient. We first characterize the drug reimbursement contract of a firm producing a new treatment with a companion genetic test when the firm can undertake an effort to improve drug quality. Then, we determine the conditions under which personalized medicine should be implemented when this effort is observable and when it is not. Finally, we show how the unobservability of effort affects the conditions under which the health authority implements personalized medicine
31
Cornec-Le, Gall Emilie. "Polykystose rénale autosomique dominante : de la génétique moléculaire au développement d'outils pronostiques". Thesis, Brest, 2015. http://www.theses.fr/2015BRES0030.
Abstract (sommario):
La Polykystose Rénale Autosomique Dominante (PKRAD) est une des pathologies héréditaires les plus fréquentes et affecte environ un individu sur 1000. Elle se caractérise par une importante variabilité clinique, notamment dans l’âge de survenue de l’insuffisance rénale terminale. Deux gènes sont en cause : le gène PKD1 situé sur le chromosome 16 (85% des cas) et le gène PKD2 situé sur le chromosome 4 (15% des cas). Les progrès majeurs dans la compréhension des mécanismes moléculaires impliqués ont permis le développement de stratégies thérapeutiques spécifiques, et de nouvelles questions surgissent : quels patients traiter ? Quand débuter les traitements ? La cohorte Genkyst, qui vise à inclure tous les patients suivis pour PKRAD dans la région Grand Ouest, nous a d’abord permis de décrire la variabilité génétique rencontrée dans la PKRAD. Nous avons ensuite démontré l’existence de fortes corrélations génotype-phénotype, en rapportant l’influence sur l’âge de survenue de l’insuffisance rénale terminale non seulement du gène en cause, mais aussi du type de mutation pour le gène PKD1. Enfin, l’analyse des données cliniques et génétiques de 1341 patients nous a permis de développer un algorithme pronostique, baptisé le PROPKD score, permettant de stratifier le risque de progression vers l’insuffisance rénale terminale. Nous espérons que ces travaux participeront à l’individualisation de la prise en charge des patients atteints de PKRAD, ce qui est un enjeu crucial à l’arrivée des nouveaux traitementsAutosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most frequent Mendelian inherited disorders, and affects approximately one individual out of 1000. ADPKD is marked by a high clinical variability, especially regarding age at end-stage renal disease (ESRD). Two genes are identified: PKD1 located on the chromosome 16 (85% of the pedigrees) and PKD2 located on the chromosome 4 (15% of the pedigrees). Substantial progress in understanding the cellular mechanisms underlying ADPKD has triggered the development of targeted therapies, and new questions are arising: which patients should be treated? When should we begin these treatments? Thanks to Genkyst cohort, which aims to include all consenting ADPKD patients from the western part of France, we first described the important allelic variability encountered in ADPKD. Secondly, we demonstrated the important influence of not only the gene involved, but also of PKD1 mutation type. Last, the analysis of clinical and genetic characteristics of 1341 patients from the Genkyst cohort allowed us to develop a prognostic algorithm, named the PROPKD score for predicting renal outcome in ADPKD. Our hope is that these works will participate in the development of individualized medicine in ADPKD, which is crucial in the context of the emerging targeted therapies
32
Fritz, Justine. "Validation préclinique d'un test de prédiction d'efficacité de médicaments anti-cancéreux : application au glioblastome, cancer colorectal et cancer de la prostate". Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAJ058.
Abstract (sommario):
Nous avons développé un nouveau test de prédiction de l’efficacité de thérapies anti-cancéreuses. Ce concept se base sur la détermination d’une signature moléculaire tumorale par RT-qPCR. Cette signature est issue d’un algorithme de normalisation innovant de comparaison des données d’expression relative des cibles de la tumeur avec celles de tissus de référence. Cette normalisation offre à chaque cible de la signature un rang et un score spécifique permettant de hiérarchiser les voies pro-tumorales afin de trouver la ou les cibles dominantes responsables du développement de la tumeur. La signature comprend des cibles donnant des informations générales sur le statut et l’hétérogénéité de la tumeur, sur l’angiogenèse et la lymphangiogenèse, sur le microenvironnement tumoral et enfin sur l’activité migratoire. Une validation préclinique dans les modèles du cancer colorectal, de la prostate et du glioblastome, a montré que le test est prédictif de l’efficacité thérapeutiqueWe developed a new tool for prediction of cancer treatment efficacy. Our process is based on the determination of the molecular signature which is intended to provide a clinician’s decision tool helping to select which tumor signaling pathway(s) has/have to be targeted for best therapeutic effect. This signature representing a scoring obtained by RT-qPCR through a sequential normalization process of the expression level of target genes in the tumor compared to cancer type-specific references. These genes were selected because of a good knowledge of related biological functions, a correlation between expression level and aggressiveness of the tumor, the existence of a therapeutic arsenal already in clinical use. This signature is validated in a preclinical model of colorectal cancer and prostate cancer and glioblastoma. The results obtained show that the test we developed allows to identify the most important signaling pathway implicated in the disease and to choose the best drug
33
Prague, Mélanie. "Utilisation des modèles dynamiques pour l'optimisation des traitements des patients infectés par le VIH". Thesis, Bordeaux 2, 2013. http://www.theses.fr/2013BOR22056.
Abstract (sommario):
La plupart des patients infectés par le VIH ont une charge virale qui peut être rendue indétectable par des combinaisons antirétrovirales hautement actives (cART); cependant, il existe des effets secondaires aux traitements. L'utilisation des modèles mécanistes dynamiques basés sur des équations différentielles ordinaires (ODE) a considérablement amélioré les connaissances de la dynamique HIV-système immunitaire et permet d'envisager une personnalisation du traitement. L'objectif de ces travaux de thèse est d'améliorer les techniques statistiques d'estimation de paramètres dans les modèles mécanistes dynamiques afin de proposer des stratégies de surveillance et d'optimisation des traitements. Après avoir introduit NIMROD un algorithme d'estimation bayésienne basé sur une maximisation de la vraisemblance pénalisée, nous montrons la puissance des approches mécanistes dynamiques pour l'évaluation des effets traitements par rapport aux méthodes descriptives d'analyse des trajectoires des biomarqueurs. Puis, nous définissons le « modèle à cellules cibles », un système ODE décrivant la dynamique du VIH et des CD4. Nous montrons qu'il possède de bonnes capacités prédictives. Nous proposons une preuve de concept de la possibilité de contrôler individuellement la dose de traitement. Cette stratégie adaptative réajuste la dose du patient en fonction de sa réaction à la dose précédente par une procédure bayésienne. Pour finir, nous introduisons la possibilité de l’'individualisation des changements de cART. Ce travail passe par la quantification in vivo d'effets de cART en utilisant des indicateurs d'activité antivirale in vitro. Nous discutons la validité des résultats et les étapes méthodologiques nécessaires pour l'intégration de ces méthodes dans les pratiques cliniquesMost HIV-infected patients viral loads can be made undetectable by highly active combination of antiretroviral therapy (cART), but there are side effects of treatments. The use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system and can be considered for personalization of treatment. The aim of these PhD works is to improve the statistical techniques for estimating parameters in dynamic mechanistic models so as to elaborate strategies for monitoring and optimizing treatments. We present an algorithm and program called NIMROD using Bayesian inference based on the maximization of the penalized likelihood. Then, we show the power of dynamic mechanistic approaches for the evaluation of treatment effects compared to methods based on the descriptive analysis of the biomarkers trajectories. Next, we build the “target cells model “, an ODE system of the dynamics between the HIV and CD4. We demonstrate it has good predictive capabilities. We build a proof of concept for drug dose individualization. It consists in tuning the dose of the patient based on his reaction to the previous doses using a Bayesian update procedure. Finally, we introduce the possibility of designing an individualized change of cART. This work involves the quantification of in vivo effects of cART using in vitro antiviral activity indicators. We discuss the validity of the results and the further steps needed for the integration of these methods in clinical practice
34
Jiang, Tian. "Personalized Defect Prediction". Thesis, 2013. http://hdl.handle.net/10012/7786.
Abstract (sommario):
Academia and industry expend much effort to predict software defects. Researchers proposed many defect prediction algorithms and metrics. While previous defect prediction techniques often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, which would result in different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting the prediction performance.
This thesis proposes two techniques to improve defect prediction performance: personalized defect prediction and confidence-based hybrid defect prediction. Personalized defect prediction builds a separate prediction model for each developer to predict software defects. Confidence-based hybrid defect prediction combines different models by picking the prediction from the model with the highest confidence. As a proof of concept, we apply the two techniques to classify defects at the file change level. We implement the state-of-the-art change classification as the baseline and compare with the personalized defect prediction approach. Confidence-based defect prediction combines these two models. We evaluate on six large and popular software projects written in C and Java—the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit.
35
Kuan, Mei-Lan, e 官美蘭. "Comprehensive and Personalized Stock Performance Prediction". Thesis, 1999. http://ndltd.ncl.edu.tw/handle/27211154926656315250.
Abstract (sommario):
碩士輔仁大學
資訊管理學系
87
This thesis aims to find new value-added services for traditional stock systems based on neural networks. For example, they are capable of soliciting valuable information related to why the stock prediction is made, and furthermore offer investors personalized stock prediction. That is, we attempt to build a comprehensive and personalized stock prediction system, which can become a personalized financial consultant for investors. There are two major components in this system. The first component is a comprehensive component, which is built on the rule extraction methodology and is responsible for providing comprehensive prediction results to investors. The second component is composed of the Eureka financial multi-agent system and a personalized component, which is based on the knowledge-based neural network methodology and is able to allow investors to build customized prediction models. From the experiment results, the extra values provided by the comprehensive and personalized components shed light on the future trend of stock performance prediction systems in terms of the provision of customized prediction and the gain of confidence in the stock prediction.
36
YEH, CHAN-CHANG, e 葉展彰. "Building a Personalized Music Emotion Prediction System". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/87377903598187527173.
37
Wang, Wei-Chun, e 王威淳. "Personalized Dynamic Prediction Model for Hepatocellular Carcinoma". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q555ed.
Abstract (sommario):
碩士國立臺灣大學
流行病學與預防醫學研究所
105
Background The predictive model for the risk of hepatocellular carcinoma (HCC) has been developed in previous studies but such kinds of models are often presented for predicting the mean risk of the underlying population. More importantly, the translation of these predictive models into a personalized prediction model for individual risk of hepatocellular carcinoma (HCC) has increasingly gained attention. The objectives of this thesis are to (1) to build up a predictive model for individual risk prediction for HCC by using a Bayesian clinical reasoning algorithm in order to stratify risk groups for average-risk subjects and high-risk hepatitis B carrier; (2) to build up a dynamic prediction model, considering the dynamics of HBV DNA level and ALT level for hepatitis B carrier and time-varying covariates (including AST, ALT, AFP, AC sugar, and platelet) for average-risk subjects, for the risk of HCC with time-dependent Cox regression model; (3) to build up a dynamic risk-score-based prediction model with the formulation of risk score based on the same information on time-varying covariates for hepatitis B carrier as seen in (2) in order to elucidate the dynamics of intermediate events defined by risk-score-based categories and also to predict the final outcome of HCC. Materials and Methods Two study cohorts were enrolled including a community-based screening cohort for general population between 1999 and 2007 and a hospital-based high-risk (i.e. hepatitis B carrier) cohort. For the community cohort, a two-stage design for liver cancer screening were provided for 98552 subjects. Information on HBV and HCV infection status, liver function test, AFT, family history of liver cancer, demographic characteristics, life style variables and relevant biomarkers were collected. Subjects detected as high risk received abdominal ultrasonography for detecting HCC at the intervals of three and six months, depending on the level of risk. The occurrence of HCC were ascertain by the linkage of the nationwide cancer registry till the end of 2007. Considering the REVEAL-HBV hospital-based high-risk population, 3584 subjects who were HBV carriers and free of HCC were enrolled and received regular surveillance of HCC. In addition to the information mentioned above, HBV genotype were also measured. Confirmatory diagnosis of HCC were provided to subject with clinical suspicion. For the derivation of individual-tailored risk stratification and prediction, a series of statistical approaches including the conventional models and multistate models were applied. Due to the updated information derived from repeated evaluation of biomarkers such as AST, ALT, and platelet count, a time-varying Cox regression model was applied to derive the risk scores used for the following Markov model analysis. For the derivation of dynamic process along the evolution of HCC, four-state Markov models using HBV DNA vial loading and the risk scores derived from the results of time-dependent Cox model were regarded as the definition of state space. Results The findings on the identification of risk factors were consistently noted in logistic regression, Cox proportional hazards regression, and time-dependent Cox regression models. Using Bayesian clinical reasoning algorithm, the posterior individual risk of HCC could be updated to a range between 0.10% and 80%. In the high-risk population of HBV carrier, REVEL cohort, the adjusted hazard ratio (aHR) of baseline HBV DNA in the levels of 300-9999, 10^4-99999, 10^5-999999, and >= 10^6 increased from 1.12 (95% CI: 0.62-2.03) to 5.63 (95% CI: 3.13-10.13) compared to those <300 copies/mL. The aHR for ALT >= 45 IU/L was 1.84 (95% CI: 1.21-2.78) than ALT<45 IU/L. These figure were inflated when applying the dynamic value in the repeated examination [1.79 (95% CI: 1.06-3.03) to 5.99 (95% CI: 3.58-10.01) for HBV DNA, and 2.46 (95% CI: 1. 59-3.82) for ALT >= 45 IU/L]. A risk score based on the multivariable time-dependent Cox model was derived. In the four-state Markov model, the progression rates from low- to intermediate- and from intermediate- to high-risk group were 4.4% (95% CI: 4-4.8%) and 3% (95% CI: 2.6-3.3%), respectively. The regression rates from intermediate- to low- and from high- to intermediate-risk group were 8% (95% CI: 7.8-9.1%) and 13% (95% CI: 11-14%). The hazards rate of HCC from the high-risk group was 3.2%, which was 6-fold than the intermediate risk group. The hazard rate of HCC for the intermediate risk group was about 5-fold than the low-risk group. The 12-year cumulative risk of HCC for risk score <=10, 11-14, and >=15 was 25, 69, and 205 per 1000, respectively, making allowance for three transients states pertaining to dynamics of risk-score group (including time-invariant and time-varying covariates). Conclusions We developed a novel personalized dynamic predictive model for the risk for HCC among Taiwanese subjects. The proposed dynamic prediction models are not only useful for the risk classification of HCC and also useful for the surveillance of personalized treatment to HBV.
38
Abedtash, Hamed. "An interoperable electronic medical record-based platform for personalized predictive analytics". Diss., 2017. http://hdl.handle.net/1805/13759.
Abstract (sommario):
Indiana University-Purdue University Indianapolis (IUPUI)Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications.
39
Dai, Wuyang. "Detection and prediction problems with applications in personalized health care". Thesis, 2015. https://hdl.handle.net/2144/15651.
Abstract (sommario):
The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations.
For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem.
For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice.
40
Chang, Hsiao-Pei, e 張筱珮. "Personalized Automatic Quiz Generation Based on Reading Difficulty and Proficiency Level Prediction". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/89732120361526910933.
Abstract (sommario):
碩士國立臺灣大學
資訊管理學研究所
99
A lot of research works have been done in the field of automatic quiz generation, however, almost all those studies generate all possible combination of quizzes and only few research consider difficulties of different quizzes. In this study, not only a quiz‘s difficulty but also the difference between learners and the reading difficulty of a document are taken into consideration. Therefore, we design a personalized automatic quiz generation system based on reading difficulty estimation scheme in a given document and proficiency level prediction for a second language learner. In the reading difficulty estimation scheme, we consult some meaningful lexical and grammatical features in early work, and then further consider several word frequency features from corpora, official grading indexes of vocabulary from language experts, and grammar patterns collected from textbooks — those which represent words and grammar patterns that the L2 learners have learned at various grade levels. In the proficiency level prediction, we estimate a learner‘s ability from three dimensions, which are vocabulary ability, grammar ability, and reading comprehension ability, and then further consider his historical performance to determine his proficiency level by weighted exponential moving average. A personalized news reading and testing experiment was conducted. The experimental results show that the proposed estimation outperforms the other estimations, and is close to the annotation of human experts. Moreover, It also shows that our system can increase learners‘ English proficiency, and provide a good prediction of learners‘ proficiency level.
41
Hwang, Susan. "Similarity-principle-based machine learning method for clinical trials and beyond". Thesis, 2020. https://hdl.handle.net/2144/41983.
Abstract (sommario):
The control of type-I error is a focal point for clinical trials. On the other hand, it is also critical to be able to detect a truly efficacious treatment in a clinical trial. With recent success in supervised learning (classification and regression problems), artificial intelligence (AI) and machine learning (ML) can play a vital role in identifying efficacious new treatments. However, the high performance of the AI methods, particularly the deep learning neural networks, requires a much larger dataset than those we commonly see in clinical trials. It is desirable to develop a new ML method that performs well with a small sample size (ranges from 20 to 200) and has advantages as compared with the classic statistical models and some of the most relevant ML methods.
In this dissertation, we propose a Similarity-Principle-Based Machine Learning (SBML) method based on the similarity principle assuming that identical or similar subjects should behave in a similar manner. SBML method introduces the attribute-scaling factors at the training stage so that the relative importance of different attributes can be objectively determined in the similarity measures. In addition, the gradient method is used in learning / training in order to update the attribute-scaling factors. The method is novel as far as we know.
We first evaluate SBML for continuous outcomes, especially when the sample size is small, and investigate the effects of various tuning parameters on the performance of SBML. Simulations show that SBML achieves better predictions in terms of mean squared errors or misclassification error rates for various situations under consideration than conventional statistical methods, such as full linear models, optimal or ridge regressions and mixed effect models, as well as ML methods including kernel and decision tree methods.
We also extend and show how SBML can be flexibly applied to binary outcomes. Through numerical and simulation studies, we confirm that SBML performs well compared to classical statistical methods, even when the sample size is small and in the presence of unmeasured predictors and/or noise variables.
Although SBML performs well with small sample sizes, it may not be computationally efficient for large sample sizes. Therefore, we propose Recursive SBML (RSBML), which can save computing time, with some tradeoffs for accuracy. In this sense, RSBML can also be viewed as a combination of unsupervised learning (dimension reduction) and supervised learning (prediction). Recursive learning resembles the natural human way of learning. It is an efficient way of learning from complicated large data. Based on the simulation results, RSBML performs much faster than SBML with reasonable accuracy for large sample sizes.
42
Kureshi, Nelofar. "Personalized Medicine: Development of a Predictive Computational Model for Personalized Therapeutic Interventions". 2013. http://hdl.handle.net/10222/35383.
Abstract (sommario):
Lung cancer is the leading cause of cancer-related deaths among men and women. Non-Small Cell Lung Cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. In the past decade, discovery of Epidermal Growth Factor Receptor (EGFR) mutations have heralded a new paradigm of personalized treatment for NSCLC. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Despite these advances, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. This thesis demonstrates the development of a predictive computational model for personalized therapeutic interventions in advanced NSCLC. The findings suggest that the combination of clinical and genetic data significantly improves the model’s predictive performance for tumor response than clinical data alone.
43
Banjar, Haneen Reda. "Personalized Medicine Support System for Chronic Myeloid Leukemia Patients". Thesis, 2018. http://hdl.handle.net/2440/117837.
Abstract (sommario):
Personalized medicine offers the most effective treatment protocols to the individual Chronic Myeloid Leukemia (CML) patients. Understanding the molecular biology that causes CML assists in providing efficient treatment. After the identification of an activated tyrosine kinase BCR-ABL1 as the causative lesion in CML, the first-generation Tyrosine Kinase inhibitors (TKI) imatinib (Glivec®), were developed to inhibit BCR-ABL1 activity and approved as a treatment for CML. Despite the remarkable increase in the survival rate of CML patients treated with imatinib, some patients discontinued imatinib therapy due to intolerance, resistance or progression. These patients may benefit from the use of secondgeneration TKIs, such as nilotinib (Tasigna®) and dasatinib (Sprycel®). All three of these TKIs are currently approved for use as frontline treatments. Prognostic scores and molecularbased predictive assays are used to personalize the care of CML patients by allocating risk groups and predicting responses to therapy. Although prognostic scores remain in use today, they are often inadequate for three main reasons. Firstly, since each prognostic score may generate conflicting prognoses for the risk index and it can be difficult to know how to treat patients with conflicting prognoses. Secondly, since prognostic score systems are developed over time, patients can benefit from newly developed systems and information. Finally, the earlier scores use mostly clinically oriented factors instead of those directly related to genetic or molecular indicators. As the current CML treatment guidelines recommend the use of TKI therapy, a new tool that combines the well-known, molecular-based predictive assays to predict molecular response to TKI has not been considered in previous research. Therefore, the main goal of this research is to improve the ability to manage CML disease in individual CML patients and support CML physicians in TKI therapy treatment selection by correctly allocating patients to risk groups and predicting their molecular response to the selected treatment. To achieve this objective, the research detailed here focuses on developing a prognostic model and a predictive model for use as a personalized medicine support system. The system will be considered a knowledge-based clinical decision support system that includes two models embedded in a decision tree. The main idea is to classify patients into risk groups using the prognostic model, while the patients identified as part of the high-risk group should be considered for more aggressive imatinib therapy or switched to secondgeneration TKI with close monitoring. For patients assigned to the low-risk group to imatinib should be predicted using the predictive model. The outcomes should be evaluated by comparing the results of these models with the actual responses to imatinib in patients from a previous medical trial and from patients admitted to hospitals. Validating such a predictive system could greatly assist clinicians in clinical decision-making geared toward individualized medicine. Our findings suggest that the system provides treatment recommendations that could help improve overall healthcare for CML patients. Study limitations included the impact of diversity on human expertise, changing predictive factors, population and prediction endpoints, the impact of time and patient personal issues. Further intensive research activities based on the development of a new predictive model and the method for selecting predictive factors and validation can be expanded to other health organizations and the development of models to predict responses to other TKIs.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
44
WU, PING-HUAN, e 吳秉桓. "The Study of Constructing Personalized Portfolio and Predicting Fund Performance". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/54164118355826776293.
Abstract (sommario):
碩士輔仁大學
資訊管理學系
96
The objective of this study is to find the best performance of funds through the classification techniques of data mining. This study uses classification algorithms to construct prediction models as well as combines technical indices and fund attributes for analysis. A secondary purpose of this study is to help investors finding the personalized portfolio through genetic algorithms of evolutionary computation. The results obtained in this study have indicated that decision tree algorithm has better performance than others in constructing prediction models. The accuracy of prediction and the length of investing period have been shown to be positively correlated with one anther. Comparatively, the performance of funds chosen by prediction models surpassed the price index far on the rate of returns. The results also show that this study has great performance in efficiency and effectiveness with genetic algorithms. At effectiveness, the results show that average fitness, best fitness, root mean square error, and return on investment are satisfied. This study considers users’ preferences and performance of investment portfolio at the same time, hoping to support investors making their suitable decisions. The results reported in this paper have demonstrated that this system can be fast evolved based on both high rate of returns and users’ preferences through the pre-training prediction models and genetic algorithms.
45
Guggenheim, J. A., Mojarrad Neema Ghorbani, C. Williams e D. I. Flitcroft. "Genetic prediction of myopia: prospects and challenges". 2017. http://hdl.handle.net/10454/17506.
Abstract (sommario):
YesAppeals have been made for eye care professionals to start prescribing anti-myopia therapies as part of their routine management of myopic children. 1–3 These calls are fuelled by two key considerations. Firstly, that interventions to slow myopia progression have shown success in randomized controlled trials (RCTs) 4–7, and secondly, appreciation that the risk of sight-threatening complications rises dose-dependently with the level of myopia. 8,9 Notwithstanding existing gaps in knowledge regarding the efficacy of current treatments (see below), these considerations argue that myopia control interventions should be widely adopted, and that they should be instigated at an early age – especially in children most at risk – in order to reduce the final level of myopia. Therefore in managing a child with myopia, an eye care professional would have to decide not only which therapy to recommend, but at what age to start treatment. In this review we discuss the future role of genetic prediction in helping clinicians treat myopia.
NIHR Senior Research Fellowship. Grant Number: SRF‐2015‐08‐005
46
Cameron, Kellas Ross. "Studies on using data-driven decision support systems to improve personalized medicine processes". Thesis, 2018. https://hdl.handle.net/2144/30452.
Abstract (sommario):
This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions.
The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare.
The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC.
The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies.
Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes.
47
Bessa, Sílvia da Conceição Neto. "Personalized 3D Breast Cancer Models: from Multimodal Registration to Predictive Shape Modelling". Doctoral thesis, 2021. https://hdl.handle.net/10216/136835.
48
Villar, Marta Maria Cabral Menéres Posser. "Machine learning approach for personalized recommendations on online platforms: uniplaces case study". Master's thesis, 2021. http://hdl.handle.net/10362/113411.
Abstract (sommario):
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this project is to develop a model to personalize the user recommendations of an online marketplace named Uniplaces. This online business offers properties for medium and long-term stays, where landlords can directly rent their place to customers (mainly students). Whenever a student makes a reservation, the booking must be approved by the property owner. The current acceptance rate is 25%. The model is a response to this low acceptance rate, and it will have to show to each student the properties that are more likely to be accepted by the landlord. As a secondary objective, the model seeks to identify the reasons behind the landlord’s decision to accept or reject bookings. The model will be constructed using information from the users, landlord and the property itself kindly provided by Uniplaces. This information will pre-process with data cleaning, transformation and features reduction (where two techniques were applied: dimensionality reduction, features selection). After the data processing, several models were applied to the normalized data. The predictive models that will be applied are already being used on other online markets and platforms like Airbnb, Netflix or LinkedIn, namely Support Vector Machine, Neural Networks, Decision Tree, Logistic Regression and Gradient Boosting. The probability of acceptance proved to be very easy to predict, all the models predict 100% of the test dataset when using the Principal Component Analysis as the Dimensionality Reduction technique. This can be explained mainly by the fact that the new calculated features have a strong correlation with the target variable. All the algorithms predict 100% of the target variable when using Principal Component Analysis as a technique of dimensionality reduction.
49
Melidis, Andreas. "Personalized marketing campaign for upselling using predictive modeling in the health insurance sector". Master's thesis, 2020. http://hdl.handle.net/10362/99076.
Abstract (sommario):
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsNowadays, with the oversupply of several different solutions in the private Health Insurance sector and the constantly increasing demand for value for money services from the client’s perspective, it becomes clear that Insurance Companies shouldn’t only strive for excellence but also engage their client base by offering solutions that are more suitable to their needs. This project aims, using the power that predictive models can provide, to predict the existing Health Insurance clients who are willing to move in a higher tier product. The case presented above could be described under the term of upselling. The final model will be used for a personalized marketing campaign in one of the most prominent bancassurances in Portugal. At the moment the ongoing upselling campaign, uses only few eligibility criteria. The outcome of the model has as a goal to assign a probability to each client who is eligible to be contacted for this campaign. The data that were retrieved to train the model, had a buffer period of one week from when the ‘event’ took place. This is crucial for the business, because there is always the time-to-market parameter which should be taken into consideration in the real world. The tools that were used for completing this Data Mining project were mostly SAS Enterprise Guide and SAS Enterprise Miner. All the Data Marts that were needed for the particular project, were built and loaded in SAS, so there were no obstacles or connectivity issues. For data visualization and reporting, Microsoft PowerBI was used. Some of the tables in the Data Marts, are being updated in a daily and other in a monthly basis. Of course, all the historical information is being stored in separate tables, so there is no information loss or discrepancies. Finally, the methodology that was followed for the implementation of the Data Mining project was a hybrid framework between the SEMMA approach as it is the one that is proposed by SAS Institute to carry out the core tasks of model development and CRISP-DM.
50
Han, Yan. "On the Use of Marker Strategy Design to Detect Predictive Marker Effect in Cancer Immunotherapy". Thesis, 2019. http://hdl.handle.net/1805/20751.
Abstract (sommario):
Indiana University-Purdue University Indianapolis (IUPUI)The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies.