NDLI: Hidden Markov Models for disease progression Hidden Markov Models per la progressione di patologia

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Hidden Markov Models for disease progression Hidden Markov Models per la progressione di patologia

Content Provider	Semantic Scholar
Author	Martino, Andrea Di Ghiglietti, Andrea Guatteri, Giuseppina Paganoni, Anna Maria
Copyright Year	2018
Abstract	Disease progression models are a powerful tool for understanding and predicting the development of a disease, given some longitudinal measurements obtained from a sample of patients. These models are able to give some insights about the disease progression through the analysis of patients histories and could be also used to predict the future course of the disease in an individual. In particular, Hidden Markov Models (HMMs) are a useful tool for disease modeling since they allow to model situations where the state of the disease is not observable, by giving the possibility to incorporate some priors and constraints. We applied our models to a simulated dataset by considering a generalization of HMMs with continuous time and multivariate outcome. Abstract I modelli di progressione di patologia sono un potente strumento per comprendere e prevedere lo sviluppo di una patologia, date delle misurazioni longitudinali ottenute da un campione di pazienti. Questi modelli sono in grado di arricchire la conoscenza sulla progressione della patologia attraverso l’analisi della storia dei pazienti e possono anche essere usati per predire il corso futuro della patologia di un individuo. In particolare, gli Hidden Markov Models (HMMs) sono un utile strumento per la modellazione di patologia in quanto permettono di costruire un modello in situazioni in cui lo stato della patologia non è osservabile, dando la possibilità di incorporare delle prior e dei vincoli. Abbiamo applicato i nostri modelli a un dataset simulato considerando una generalizzazione degli HMM a tempo continuo e risposta multivariata. Andrea Martino, Giuseppina Guatteri, Anna Maria Paganoni Department of Mathematics, Politecnico di Milano, via Bonardi 9, 20133, Milan, Italy e-mail: andrea.martino@polimi.it e-mail: giuseppina.guatteri@polimi.it e-mail: anna.paganoni@polimi.it Andrea Ghiglietti Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123, Milan, Italy e-mail: andrea.ghiglietti@unicatt.it
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://meetings3.sis-statistica.org/index.php/sis2018/49th/paper/viewFile/1341/190
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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