Phenotype Inference with Semi-Supervised Mixed Membership Models

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.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 https://urldefense.proofpoint.com/v2/url?u=https-3A__arxiv.org_abs_1811.07216&d=DwIEaQ&c=G2MiLlal7SXE3PeSnG8W6_JBU6FcdVjSsBSbw6gcR0U&r=V4N8fh0BvUFXSfHS_5FyHekWwaQfwQATAFihsExKikM&m=3ZpgK5EnFWR2OpdKWz1sCspjnLWOElIt_VHn1RMHJ5U&s=iE0HP7cbAigUopbIm2O8hByUTkVYvOw7R2y8QoF6GRg&e