  Modeling continuous-time physiological processes that manifest a patient's
evolving clinical states is a key step in approaching many problems in
healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model
(HASMM): a versatile probabilistic model that is capable of capturing the
modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM
accommodates irregularly sampled, temporally correlated, and informatively
censored physiological data, and can describe non-stationary clinical state
transitions. Learning an HASMM from the EHR data is achieved via a novel
forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the
knowledge of the end-point clinical outcomes (informative censoring) in the EHR
data, and implements the E-step by sequentially sampling the patients' clinical
states in the reverse-time direction while conditioning on the future states.
Real-time inferences are drawn via a forward- filtering algorithm that operates
on a virtually constructed discrete-time embedded Markov chain that mirrors the
patient's continuous-time state trajectory. We demonstrate the di- agnostic and
prognostic utility of the HASMM in a critical care prognosis setting using a
real-world dataset for patients admitted to the Ronald Reagan UCLA Medical
Center.
