  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.
