An analytics approach to hypertension treatment

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Epstein, Christina (Christina Lynn)

Advisor dc:contributor.advisor

• Dimitris J. Bertsimas.

• Operations Research Center.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/91299

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/91299