Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
This work addresses the problem of capturing diverse disease patterns for neurodegenerative disorders like Parkinson's disease, representing an incremental improvement over existing methods.
The authors tackled the challenge of modeling heterogeneous disease progression by proposing a hierarchical time-series model that discovers multiple dynamics, demonstrating its benefits on synthetic and real-world Parkinson's disease datasets.
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.