Rozprawy doktorskie na temat „Personalized prediction”
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Fernando, Warnakulasuriya Chandima. "Blood Glucose Prediction Models for Personalized Diabetes Management". Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28179.
Pełny tekst źródłaShen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.
Pełny tekst źródłaBiostatistics
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.
Pełny tekst źródłaIl 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/.
Pełny tekst źródłaYoussfi, 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.
Pełny tekst źródłaSudden 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.
Pełny tekst źródłaAdverse 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.
Pełny tekst źródłaLevillain, 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.
Pełny tekst źródłaDoctorat 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.
Pełny tekst źródłaMohammed, 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.
Pełny tekst źródłaCheng, 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.
Pełny tekst źródłaAlderdice, 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.
Pełny tekst źródłaGilholm, 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.
Pełny tekst źródłaPark, 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.
Pełny tekst źródłaFu, 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.
Pełny tekst źródłaIntegrated 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
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.
Pełny tekst źródłaBragazzi, Nicola Luigi [Verfasser], i 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.
Pełny tekst źródłaIANZA, 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.
Pełny tekst źródłaTsur, Neta [Verfasser], i 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.
Pełny tekst źródłaBiasci, Daniele. "Predicting prognosis in Crohn's disease". Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270034.
Pełny tekst źródłaGil, 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.
Pełny tekst źródłaEl 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.
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.
Pełny tekst źródłaSalehe, 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/.
Pełny tekst źródłaAlyamani, 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.
Pełny tekst źródłaWang, 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.
Pełny tekst źródłaKim, Jin Hee. "Functional genomics of cardiovascular disease risk". Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51769.
Pełny tekst źródłaHoogendoorn, 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.
Pełny tekst źródłaEsta 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.
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.
Pełny tekst źródłaGallart, 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.
Pełny tekst źródłaEste 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.
Alcenat, Stéphane. "Assurance maladie et tests génétiques". Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCB002.
Pełny tekst źródłaThis 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
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.
Pełny tekst źródłaAutosomal 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
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.
Pełny tekst źródłaWe 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
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.
Pełny tekst źródłaMost 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
Jiang, Tian. "Personalized Defect Prediction". Thesis, 2013. http://hdl.handle.net/10012/7786.
Pełny tekst źródłaKuan, Mei-Lan, i 官美蘭. "Comprehensive and Personalized Stock Performance Prediction". Thesis, 1999. http://ndltd.ncl.edu.tw/handle/27211154926656315250.
Pełny tekst źródła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.
YEH, CHAN-CHANG, i 葉展彰. "Building a Personalized Music Emotion Prediction System". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/87377903598187527173.
Pełny tekst źródłaWang, Wei-Chun, i 王威淳. "Personalized Dynamic Prediction Model for Hepatocellular Carcinoma". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q555ed.
Pełny tekst źródła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.
Abedtash, Hamed. "An interoperable electronic medical record-based platform for personalized predictive analytics". Diss., 2017. http://hdl.handle.net/1805/13759.
Pełny tekst źródłaPrecision 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.
Dai, Wuyang. "Detection and prediction problems with applications in personalized health care". Thesis, 2015. https://hdl.handle.net/2144/15651.
Pełny tekst źródłaChang, Hsiao-Pei, i 張筱珮. "Personalized Automatic Quiz Generation Based on Reading Difficulty and Proficiency Level Prediction". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/89732120361526910933.
Pełny tekst źródła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.
Hwang, Susan. "Similarity-principle-based machine learning method for clinical trials and beyond". Thesis, 2020. https://hdl.handle.net/2144/41983.
Pełny tekst źródłaKureshi, Nelofar. "Personalized Medicine: Development of a Predictive Computational Model for Personalized Therapeutic Interventions". 2013. http://hdl.handle.net/10222/35383.
Pełny tekst źródłaBanjar, Haneen Reda. "Personalized Medicine Support System for Chronic Myeloid Leukemia Patients". Thesis, 2018. http://hdl.handle.net/2440/117837.
Pełny tekst źródłaThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
WU, PING-HUAN, i 吳秉桓. "The Study of Constructing Personalized Portfolio and Predicting Fund Performance". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/54164118355826776293.
Pełny tekst źródła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.
Guggenheim, J. A., Mojarrad Neema Ghorbani, C. Williams i D. I. Flitcroft. "Genetic prediction of myopia: prospects and challenges". 2017. http://hdl.handle.net/10454/17506.
Pełny tekst źródłaAppeals 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
Cameron, Kellas Ross. "Studies on using data-driven decision support systems to improve personalized medicine processes". Thesis, 2018. https://hdl.handle.net/2144/30452.
Pełny tekst źródłaBessa, 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.
Pełny tekst źródłaVillar, 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.
Pełny tekst źródłaThe 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.
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.
Pełny tekst źródłaNowadays, 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.
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.
Pełny tekst źródłaThe 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.