  We develop a personalized real time risk scoring algorithm that provides
timely and granular assessments for the clinical acuity of ward patients based
on their (temporal) lab tests and vital signs. Heterogeneity of the patients
population is captured via a hierarchical latent class model. The proposed
algorithm aims to discover the number of latent classes in the patients
population, and train a mixture of Gaussian Process (GP) experts, where each
expert models the physiological data streams associated with a specific class.
Self-taught transfer learning is used to transfer the knowledge of latent
classes learned from the domain of clinically stable patients to the domain of
clinically deteriorating patients. For new patients, the posterior beliefs of
all GP experts about the patient's clinical status given her physiological data
stream are computed, and a personalized risk score is evaluated as a weighted
average of those beliefs, where the weights are learned from the patient's
hospital admission information. Experiments on a heterogeneous cohort of 6,313
patients admitted to Ronald Regan UCLA medical center show that our risk score
outperforms the currently deployed risk scores, such as MEWS and Rothman
scores.
