Using Anchors to Estimate Clinical State without Labeled Data


We present a novel framework for learning to estimate and predict clinical state variables without labeled data. The resulting models can used for electronic phenotyping, triggering clinical decision support, and cohort selection. The framework relies on key observations which we characterize and term "anchor variables". By specifying anchor variables, an expert encodes a certain amount of domain knowledge about the problem while the rest of learning proceeds in an unsupervised manner. The ability to build anchors upon standardized ontologies and the framework’s ability to learn from unlabeled data promote generalizability across institutions. We additionally develop a user interface to enable experts to choose anchor variables in an informed manner. The framework is applied to electronic medical record-based phenotyping to enable real-time decision support in the emergency department. We validate the learned models using a prospectively gathered set of gold-standard responses from emergency physicians for nine clinically relevant variables.

Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium
Yoni Halpern
Yoni Halpern
PhD student

Google Research

David Sontag
David Sontag
Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.