Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models
This work addresses disease progression modeling for healthcare researchers using EHR data, but it is incremental as it applies an existing method to a new dataset without claiming major breakthroughs.
The researchers tackled the challenge of modeling disease progression in longitudinal electronic health record (EHR) data, which is complicated by irregular observation intervals, by applying a continuous-time hidden Markov model to a cohort of 76,888 COPD patients, resulting in interpretable outputs suitable for summarization and hypothesis generation.
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.