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

• URL: http://arxiv.org/abs/2207.11846v1
• Date: Sun, 24 Jul 2022 23:17:06 GMT
• Title: Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
• Authors: Taha Ceritli, Andrew P. Creagh, David A. Clifton
• Abstract summary: We propose a hierarchical time-series model that can discover multiple disease progression dynamics. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson's disease.
• Score: 11.768140291216769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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