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

TL;DR

This paper introduces a hierarchical input-output hidden Markov model to capture multiple disease progression patterns, addressing heterogeneity in neurodegenerative disorders like Parkinson's disease.

Contribution

It extends existing models by enabling the discovery of diverse progression dynamics in heterogeneous diseases using clinical assessments and medication data.

Findings

Model successfully identifies multiple progression patterns.

Demonstrated effectiveness on synthetic and real Parkinson's data.

Improves understanding of disease heterogeneity.

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.