2026-09-20T21:02:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/912992022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Dimitris J. Bertsimas.
Epstein, Christina (Christina Lynn)
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2014-11-04T20:28:24Z
2014-11-04T20:28:24Z
2014
2014
http://hdl.handle.net/1721.1/91299
893484090
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
13
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
Hypertension is a major public health issue worldwide, affecting more than a third of the adult population and increasing the risk of myocardial infarction, heart failure, stroke, and kidney disease. Current clinical guidelines have yet to achieve consensus and continue to rely on expert opinion for recommendations lacking a sufficient evidence base. In practice, trial and error is typically required to discover a medication combination and dosage that works to control blood pressure for a given patient. We propose an analytics approach to hypertension treatment: applying visualization, predictive analytics methods, and optimization to existing electronic health record data to (1) find conjectures parallel and potentially orthogonal to guidelines, (2) hasten response time to therapy, and/or (3) optimize therapy selection. This thesis presents work toward these goals including data preprocessing and exploration, feature creation, the discovery of clinically-relevant clusters based on select blood pressure features, and three development spirals of predictive models and results.
by Christina Epstein.
S.M.
68 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Operations Research Center.
An analytics approach to hypertension treatment
Thesis
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