Improving Clinical Decisions Using Correspondence Within and Across Electronic Health Records

Abstract:
Electronic Health Record (EHR) adoption and large-scale retrospective analyses of health care data are part of a broader conversation about health care quality and cost in the United States. Clinical decision-making aids are one method of helping to improve quality and lower cost of care. In this thesis, we present three methods of leveraging correspondences across elements in health care records to aid clinicians in making care decisions. We focus on the critical care environment, where patient state can rapidly change and many care decisions need to be made in short periods of time.

First, we introduce a method to leverage correspondences between structured fields from two different EHR systems to a shared space of clinical concepts encoded in an existing domain ontology. We use these correspondences to enable the transfer of machine learning models across different or evolving EHR systems. Second, we introduce a method to learn correspondences between structured health record data and topic distributions of clinical notes written by care team members. Finally, we present a method to characterize care processes by learning correspondences between observations of patient state and actions taken by care team members.


Bio:
Jen Gong is a Ph.D. candidate in the Data Driven Inference Group at MIT, supervised by John Guttag. Her research focuses on the application of machine learning to healthcare. She is interested in how different modalities of health care data (e.g., structured health record data, clinical notes, physiological time-series) and auxiliary sources (e.g., data from similar patient populations, expert-encoded ontologies) can be leveraged to improve clinical decision-making aids. Prior to MIT, Jen received an A.B. in Applied Mathematics from Harvard College.

Committee: John Guttag, Collin Stultz, Jenna Wiens