Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

2022/07/24 by Taha Ceritli, Ceritli, Taha, Andrew P. Creagh +3
Medicine · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parkinson's Disease Mechanisms and Treatments

paper · pdf · doi:10.48550/arxiv.2207.11846

openalex publication_date 2022/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson's disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients' health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson's disease.

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