Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models
Machine Learning
2018-12-04 v1 Populations and Evolution
Machine Learning
Abstract
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.
Cite
@article{arxiv.1812.00528,
title = {Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models},
author = {Aman Verma and Guido Powell and Yu Luo and David Stephens and David L. Buckeridge},
journal= {arXiv preprint arXiv:1812.00528},
year = {2018}
}
Comments
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216