  Disease phenotyping algorithms process observational clinical data to
identify patients with specific diseases. Supervised phenotyping methods
require significant quantities of expert-labeled data, while unsupervised
methods may learn non-disease phenotypes. To address these limitations, we
propose the Semi-Supervised Mixed Membership Model (SS3M) -- a probabilistic
graphical model for learning disease phenotypes from clinical data with
relatively few labels. We show SS3M can learn interpretable, disease-specific
phenotypes which capture the clinical characteristics of the diseases specified
by the labels provided.
