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

Aman Verma, Guido Powell, Yu Luo, David A. Stephens, David L. Buckeridge

Research output: Working paper/PreprintPreprint

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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.
Original languageEnglish
Publication statusPublished - 3 Dec 2018

Keywords

  • cs.LG
  • q-bio.PE
  • stat.ML

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