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dc.contributor.advisorDimitris Bertsimas.en_US
dc.contributor.authorZhang, Rebecca,S.M.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2020-02-10T21:37:26Z
dc.date.available2020-02-10T21:37:26Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123710
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 70-75).en_US
dc.description.abstractMachine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this domain has many difficulties, chief among which is the issue of interpretability. We focus on the medical condition of stroke, a particularly desirable problem to target because it is one of the most prevalent and yet preventable conditions affecting Americans today. In this thesis, we apply novel interpretable prediction techniques to the problem of predicting stroke presence, location, acuity, and mortality risk for patient populations at two different hospital systems. We show that using an interpretable, optimal tree-based approach is roughly as effective if not better than black-box approaches. Using the clinical learnings from these studies, we explore new interpretable methodologies designed with medical applications and their unique challenges in mind. We present a novel regression algorithm to predict outcomes when the population is comprised of notably different subpopulations, and demonstrate that this gives comparable performance with improved interpretability. Finally, we explore new natural language processing techniques for machine learning from text. We propose an alternate end-to- end framework for going from unprocessed textual data to predictions, with an interpretable linguistics-based approach to model words. Altogether, this work demonstrates the promise that new parsimonious, interpretable algorithms have in the domain of stroke and broader healthcare problems.en_US
dc.description.statementofresponsibilityby Rebecca Zhang.en_US
dc.format.extent75 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleInterpretable machine learning methods for stroke predictionen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1138021852en_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Centeren_US
dspace.imported2020-02-10T21:37:24Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentOperResen_US


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