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

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference