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

2018/12/03 by Aman Verma, Verma, Aman, Guido Powell +7
Computer Science · Mathematics · Medicine · #Bayesian Methods and Mixture Models #Chronic Disease Management Strategies #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Populations and Evolution (q-bio.PE) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1812.00528

openalex publication_date 2018/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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