An analytics approach to hypertension treatment
Name
893484090-MIT.pdf
Description
Full printable version
Size
747.3 KB
Format
Adobe PDF
Checksum (MD5)
1165617eb65cb8b0b7ce490168793d15
Author(s)
Epstein, Christina (Christina Lynn)
Advisor(s)
Dimitris J. Bertsimas.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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