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John V. Guttag. Gong, Jen J. (Jen Jian) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-09-17T15:56:57Z 2018-09-17T15:56:57Z 2018 2018 http://hdl.handle.net/1721.1/118087 1052124023 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). 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. by Jen Jian Gong. Ph.D. 112 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Improving clinical decisions using correspondences within and across electronic health records Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7af06205-b096-4107-8074-28634f5ba380"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Improving clinical decisions using correspondences within and across electronic health records</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Gong, Jen J. (Jen Jian)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>