Patient Subtyping via Learning Hidden Markov Models from Pairwise Co-occurrences in EHR Data

Patient subtyping is an effective tool to investigate clinically relevant characteristics of medical services, from which useful insights can be drawn for categorizing patients' conditions and informing disease progressions. Here we propose a hidden Markov model (HMM) based treatment of extracting patient subtypes from electronic health records (EHR). Using real-world clinical data, we show that the HMM based model can effectively identify the latent Markovian structure underlying the EHR data, and derive clinically or medically plausible patient subtypes, which can be used to categorize patients of various conditions.