Improving clinical decisions using correspondences within and across electronic health records
Name
1052124023-MIT.pdf
Description
Full printable version
Size
12.68 MB
Format
Adobe PDF
Checksum (MD5)
9622782405786dfa355d10b32d415afb
Author(s)
Gong, Jen J. (Jen Jian)
Advisor(s)
John V. Guttag.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Electronic Health Record (EHR) adoption and retrospective analyses of health care data are part of a broader conversation about health care quality and cost in the United States. Machine learning in health care can be used to develop clinical decision-making aids and assess quality of care. This can help improve quality of care while lowering cost. In this thesis, we present three methods of using different kinds of data 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 use 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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 106-112).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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