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

arXiv: https://arxiv.org/abs/2207.11846 · HTML: https://mlsanity.com/paper/2207.11846/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Clustering Interval-Censored Time-Series for Disease Phenotyping](https://mlsanity.com/paper/2102.07005/) (2021)
- [A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure](https://mlsanity.com/paper/1601.04674/) (2016)
- [Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression](https://mlsanity.com/paper/1712.00181/) (2017)
- [Mixture of hidden Markov models for accelerometer data](https://mlsanity.com/paper/1906.01547/) (2019)

## Built on by (most cited first, 4 of 4)

- [T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression](https://mlsanity.com/paper/2302.12619/) (2023)
- [Time-dependent Probabilistic Generative Models for Disease Progression](https://mlsanity.com/paper/2311.09369/) (2023)
- [Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning](https://mlsanity.com/paper/2606.24604/) (2026)
- [A Structural FHMM for Interpretable Disease Trajectories in T2DM](https://mlsanity.com/paper/2608.24328/) (2026)

## Abstract (arXiv)

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.