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Critically ill patients in regular wards are vulnerable to unanticipatedadverse events which require prompt transfer to the intensive care unit (ICU).To allow for accurate prognosis of deteriorating patients, we develop a novelcontinuous-time probabilistic model for a monitored patient's temporal sequenceof physiological data. Our model captures "informatively sampled" patientepisodes: the clinicians' decisions on when to observe a hospitalized patient'svital signs and lab tests over time are represented by a marked Hawkes process,with intensity parameters that are modulated by the patient's latent clinicalstates, and with observable physiological data (mark process) modeled as aswitching multi-task Gaussian process. In addition, our model captures"informatively censored" patient episodes by representing the patient's latentclinical states as an absorbing semi-Markov jump process. The model parametersare learned from offline patient episodes in the electronic health records viaan EM-based algorithm. Experiments conducted on a cohort of patients admittedto a major medical center over a 3-year period show that risk prognosis basedon our model significantly outperforms the currently deployed medical riskscores and other baseline machine learning algorithms.

Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis