# Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models

arXiv: https://arxiv.org/abs/1812.00528 · HTML: https://mlsanity.com/paper/1812.00528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Supervised multi-specialist topic model with applications on large-scale electronic health record data](https://mlsanity.com/paper/2105.01238/) (2021)

## Abstract (arXiv)

Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare services. In a longitudinal cohort of 76,888 patients with chronic obstructive pulmonary disease (COPD), we used a continuous-time hidden Markov model with a generalized linear model to model healthcare utilization events. We found that the fitted model provides interpretable results suitable for summarization and hypothesis generation.