Bibliografie tematiche / Personalized prediction

Letteratura scientifica selezionata sul tema "Personalized prediction"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Personalized prediction":

Galetzka, Wolfgang, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler e Andreas Stang. "Distance-Metric Learning for Personalized Survival Analysis". Entropy 25, n. 10 (30 settembre 2023): 1404. http://dx.doi.org/10.3390/e25101404.

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Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can be explained by providing the user with the most influential data points for this prediction, i.e., the closest data points and their weights. The strengths and weaknesses in terms of predictive performance are highlighted on simulated data and an application of the method on two different real-world datasets of breast cancer patients shows its competitiveness with established methods.

Thoma, Clemens. "Personalized response prediction". Nature Reviews Gastroenterology & Hepatology 15, n. 11 (2 ottobre 2018): 657. http://dx.doi.org/10.1038/s41575-018-0072-z.

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Liu, Jie, Bin Liu, Yanchi Liu, Huipeng Chen, Lina Feng, Hui Xiong e Yalou Huang. "Personalized Air Travel Prediction". ACM Transactions on Intelligent Systems and Technology 9, n. 3 (13 febbraio 2018): 1–26. http://dx.doi.org/10.1145/3078845.

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TAYEBI, MOHAMMAD A., UWE GLÄSSER, MARTIN ESTER e PATRICIA L. BRANTINGHAM. "Personalized crime location prediction". European Journal of Applied Mathematics 27, n. 3 (28 aprile 2016): 422–50. http://dx.doi.org/10.1017/s0956792516000140.

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova e S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods". Russian Journal of Cardiology 26, n. 12 (25 ottobre 2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.

Localio, A. Russell, Cynthia D. Mulrow e Michael E. Griswold. "Advancing Personalized Medicine Through Prediction". Annals of Internal Medicine 172, n. 1 (12 novembre 2019): 63. http://dx.doi.org/10.7326/m19-3010.

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Xu, Yanyu, Shenghua Gao, Junru Wu, Nianyi Li e Jingyi Yu. "Personalized Saliency and Its Prediction". IEEE Transactions on Pattern Analysis and Machine Intelligence 41, n. 12 (1 dicembre 2019): 2975–89. http://dx.doi.org/10.1109/tpami.2018.2866563.

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Vassileva, Vessela. "Prostate cancer—personalized response prediction". Nature Reviews Clinical Oncology 6, n. 11 (novembre 2009): 618. http://dx.doi.org/10.1038/nrclinonc.2009.156.

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Lee, Chuan-Chun, Chia-Jui Yen e Tsunglin Liu. "Prediction of personalized microRNA activity". Gene 518, n. 1 (aprile 2013): 101–6. http://dx.doi.org/10.1016/j.gene.2012.11.068.

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Chen, Rirong, Jieqi Zheng, Li Li, Chao Li, Kang Chao, Zhirong Zeng, Minhu Chen e Shenghong Zhang. "Metabolomics facilitate the personalized management in inflammatory bowel disease". Therapeutic Advances in Gastroenterology 14 (gennaio 2021): 175628482110644. http://dx.doi.org/10.1177/17562848211064489.

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Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Più fonti

Tesi sul tema "Personalized prediction":

Fernando, Warnakulasuriya Chandima. "Blood Glucose Prediction Models for Personalized Diabetes Management". Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28179.

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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.

Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.

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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

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.

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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.

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/.

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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

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.

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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 variables
Sudden 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

Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.

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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 interprétables
Adverse 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

Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support". Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.

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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.

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.

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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

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.

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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.

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.

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Libri sul tema "Personalized prediction":

Grech, Godfrey, e Iris Grossman, a cura di. Preventive and Predictive Genetics: Towards Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.

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Olga, Golubnitschaja, a cura di. Predictive diagnostics and personalized treatment: Dream or reality. Hauppauge, NY: Nova Science Publishers, 2009.

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Podbielska, Halina, e Marko Kapalla, a cura di. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.

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Chaari, Lotfi, a cura di. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.

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Chaari, Lotfi, a cura di. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.

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Berliner, Leonard, e Heinz U. Lemke, a cura di. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.

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Barh, Debmalya. Precision Medicine: Prediction, Prevention with Personalization. Taylor & Francis Group, 2018.

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Mansnérus, Juli, Raimo Lahti e Amanda Blick, a cura di. Personalized medicine: Legal and ethical challenges. University of Helsinki, Faculty of Law, 2020. http://dx.doi.org/10.31885/9789515169419.

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This anthology deals with the legal and ethical challenges regarding personalized (precision) medicine and healthcare. It can also be regarded as the final report of a research project on the legal and ethical aspects of personalized medicine. It complements the reported results of the consortium ‘Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes)’ which were briefly presented in the booklet entitled ‘Better, Smarter, Now: Personalised Health – From Genes to Society (pHealth)’, Academy of Finland, Helsinki 2019. The articles of this anthology are not limited to the aspects of predicting and preventing Type 1 Diabetes only, as the name of the consortium would suggest. The list of participating researchers indicates that many-sided medical expertise was represented in the consortium and, in addition, computational data analysis as well as legal and ethical issues were covered by the participating sites of research. A comprehensive examination of the issues of personalized medicine requires multidisciplinary approaches. In this anthology, the legally and ethically oriented mainstream of writings has been complemented with an article of a computer scientist in order to recognize the possibilities and challenges of machine learning when interpreting the patient’s need for help. It is our hope that this anthology would be useful both for the academic community and for the decision-makers in the fields of healthcare and (personalized) medicine. It is also advisable that the anthology would give an impetus for further research activity in these new spheres of medical law and biolaw.

Wunsch, Hannah, e Andrew A. Kramer. The role and limitations of scoring systems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0028.

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Scoring systems for critically-ill patients provide a measure of the severity of illness of patients admitted to intensive care units (ICUs). They are primarily based on patient characteristics, physiological derangement, and/or clinical assessments. Severity scores themselves allow for risk-adjusting outcomes, but they can also be used to provide a prediction of the overall risk of death, length of stay, or other outcome for critically ill patients. This allows for comparison of outcomes between different cohorts of patients or between observed and predicted ICU performance. There are a number of general ICU scoring systems that are in use. All scoring systems have limitations. Future scoring systems may include prediction of longer-term outcomes, and assimilation of granular data temporally and at the molecular level that could result in more personalized severity scores to help guide individual care decisions.

Fotiadis, Dimitrios I., Eleni I. Georga e Stelios K. Tigas. Personalized Predictive Modelling in Type1 Diabetes. Elsevier Science & Technology Books, 2017.

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Capitoli di libri sul tema "Personalized prediction":

Spöring, Francesco. "Personalized Antidepressant Prescription". In Medical Ethics, Prediction, and Prognosis, 133–47. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-11.

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Tayebi, Mohammad A., e Uwe Glässer. "Personalized Crime Location Prediction". In Social Network Analysis in Predictive Policing, 99–126. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41492-8_7.

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Emura, Takeshi, Shigeyuki Matsui e Virginie Rondeau. "Personalized Dynamic Prediction of Survival". In Survival Analysis with Correlated Endpoints, 77–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3516-7_5.

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Yeh, Chan-Chang, Shian-Shyong Tseng, Pei-Chin Tsai e Jui-Feng Weng. "Building a Personalized Music Emotion Prediction System". In Advances in Multimedia Information Processing - PCM 2006, 730–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922162_84.

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Bahi, Abderaouf, Ibtissem Gasmi e Sassi Bentrad. "Personalized Movie Recommendation Prediction Using Reinforcement Learning". In Communications in Computer and Information Science, 46–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43838-7_4.

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Purushotham, Sanjay, e C. C. Jay Kuo. "Modeling Group Dynamics for Personalized Group-Event Recommendation". In Social Computing, Behavioral-Cultural Modeling, and Prediction, 405–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_51.

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Zhang, Ling, Le Lu, Ronald M. Summers, Electron Kebebew e Jianhua Yao. "Personalized Pancreatic Tumor Growth Prediction via Group Learning". In Lecture Notes in Computer Science, 424–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_48.

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Fuerst, B., T. Mansi, Jianwen Zhang, P. Khurd, J. Declerck, T. Boettger, Nassir Navab, J. Bayouth, Dorin Comaniciu e A. Kamen. "A Personalized Biomechanical Model for Respiratory Motion Prediction". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 566–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_70.

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Zhang, Lei, Jian Tang e Ming Zhang. "Integrating Temporal Usage Pattern into Personalized Tag Prediction". In Web Technologies and Applications, 354–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29253-8_30.

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Wu, Yao, Hong Huang e Hai Jin. "Information Diffusion Prediction with Personalized Graph Neural Networks". In Knowledge Science, Engineering and Management, 376–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55393-7_34.

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Atti di convegni sul tema "Personalized prediction":

Jiang, Tian, Lin Tan e Sunghun Kim. "Personalized defect prediction". In 2013 IEEE/ACM 28th International Conference on Automated Software Engineering (ASE). IEEE, 2013. http://dx.doi.org/10.1109/ase.2013.6693087.

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Whitehill, Jacob, e Javier R. Movellan. "Personalized facial attractiveness prediction". In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813332.

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Suzuki, Masahiro, Shomu Furuta e Yusuke Fukazawa. "Personalized human mobility prediction for HuMob challenge". In HuMob-Challenge '23: 1st International Workshop on the Human Mobility Prediction Challenge. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615894.3628501.

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Losing, Viktor, Barbara Hammer e Heiko Wersing. "Personalized maneuver prediction at intersections". In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317760.

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Wang, Chung-Che, Yu-Chun Lin, Yu-Teng Hsu e Jyh-Shing Roger Jang. "Personalized Audio Quality Preference Prediction". In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2023. http://dx.doi.org/10.1109/apsipaasc58517.2023.10317345.

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Bhoi, Suman, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang e Ngiap Chuan Tan. "Chronic Disease Management with Personalized Lab Test Response Prediction". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/699.

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Abstract (sommario):

Chronic disease management involves frequent administration of invasive lab procedures in order for clinicians to determine the best course of treatment regimes for these patients. However, patients are often put off by these invasive lab procedures and do not follow the appointment schedules. This has resulted in poor management of their chronic conditions leading to unnecessary disease complications. An AI system that is able to personalize the prediction of individual patient lab test responses will enable clinicians to titrate the medications to achieve the desired therapeutic outcome. Accurate prediction of lab test response is a challenge because these patients typically have co-morbidities and their treatments might influence the target lab test response. To address this, we model the complex interactions among different medications, diseases, lab test response, and fine-grained dosage information to learn a strong patient representation. Together with information from similar patients and external knowledge such as drug-lab interactions and diagnosis-lab interaction, we design a system called KALP to perform personalized prediction of patients’ response for a target lab result and identify the top influencing factors for the prediction. Experiment results on real-world datasets demonstrate the effectiveness of KALP in reducing prediction errors by a significant margin. Case studies show that the identified factors are consistent with clinicians’ understanding.

Cheng, Haibin, e Erick Cantú-Paz. "Personalized click prediction in sponsored search". In the third ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1718487.1718531.

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Chen, Guangyi, Junlong Li, Nuoxing Zhou, Liangliang Ren e Jiwen Lu. "Personalized Trajectory Prediction via Distribution Discrimination". In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01529.

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Khademi, Aria, Yasser El-Manzalawy, Orfeu M. Buxton e Vasant Honavar. "Toward personalized sleep-wake prediction from actigraphy". In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2018. http://dx.doi.org/10.1109/bhi.2018.8333456.

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Wenqi You, Alena Simalatsar e Giovanni De Micheli. "Parameterized SVM for personalized drug concentration prediction". In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610867.

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Rapporti di organizzazioni sul tema "Personalized prediction":

Manski, Charles. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. Cambridge, MA: National Bureau of Economic Research, ottobre 2021. http://dx.doi.org/10.3386/w29358.

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Zhang, Yu, Chaoliang Sun, Hengxi Xu, Weiyang Shi, Luqi Cheng, Alain Dagher, Yuanchao Zhang e Tianzi Jiang. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, aprile 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.

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Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD patients: 248 patients with typical cortical-striato-thalamic dysfunctions and 41 patients showing weakened dorsal raphe nucleus (DRN)-to-cortical/striatal projections. The proposed subtyping approach demonstrated high stability in terms of random sampling of healthy or diseased population and longitudinal prediction at follow-up visits, outperforming the traditional motor phenotypes. Compared to the typical PD, patients with the DRN-predominant subtype were characterized by less server motor symptoms at baseline and distinct imaging biomarkers, including larger striatal volumes, higher concentration of cerebrospinal fluid amyloid-β and amyloid-β/t(p)-tau ratio. Subtype-specific associations and drug effects were identified that the DRN subtype exhibited more pronounced medication effects on motor symptoms, potentially regulated by DRN serotonergic modulation through striatal dopaminergic neurons. The DRN serotonergic inputs also regulated non-motor symptoms, the aggregation of CSF biomarkers and the conversion to more severe disease states. Our findings suggest that the DRN-predominant subtype represents a unique clinical and biological phenotype of PD characterized by an enhanced response to anti-parkinsonian treatment, more favorable prognosis and slower progression of dopamine depletion. This study may contribute to clinical practice of precision medicine, early invention and individualized treatments in PD and other neurodegenerative diseases.

Making personalised predictions of poor functioning following negative childhood experiences. ACAMH, dicembre 2020. http://dx.doi.org/10.13056/acamh.14059.

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Experiencing abuse, neglect, bullying, or domestic violence in childhood increases the likelihood of having poor functioning in young adulthood, but this is not the case for everyone. Being able to accurately predict which individuals are at high risk for poor outcomes following such negative childhood experiences could support professionals to effectively target interventions. Is it possible to make accurate personalised predictions?