  Critically ill patients in regular wards are vulnerable to unanticipated
clinical dete- rioration which requires timely transfer to the intensive care
unit (ICU). To allow for risk scoring and patient monitoring in such a setting,
we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the
inpatients' physiol- ogy. The model captures the patients' latent clinical
states and their corresponding observable lab tests and vital signs. We present
an efficient unsupervised learn- ing algorithm that capitalizes on the
informatively censored data in the electronic health records (EHR) to learn the
parameters of the SSLGM; the learned model is then used to assess the new
inpatients' risk for clinical deterioration in an online fashion, allowing for
timely ICU admission. Experiments conducted on a het- erogeneous cohort of
6,094 patients admitted to a large academic medical center show that the
proposed model significantly outperforms the currently deployed risk scores
such as Rothman index, MEWS, SOFA and APACHE.
