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