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dc.contributor.advisorDimitris Bertsimas.en_US
dc.contributor.authorWeinstein, Alexander Michaelen_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2017-10-30T15:04:16Z
dc.date.available2017-10-30T15:04:16Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/112011
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.en_US
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.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 107-110).en_US
dc.description.abstractThe past few decades have seen many methodological and technological advances in optimization, statistics, and machine learning. What is still not well understood is how to combine these tools to take data as inputs and give decisions as outputs. The healthcare arena offers fertile ground for improvement in data-driven decisionmaking. Every day, medical researchers develop and test novel therapies via randomized clinical trials, which, when designed efficiently, can provide evidence for efficacy and harm. Over the last two decades, electronic medical record systems have become increasingly prevalent in hospitals and other care settings. The growth of these and other data sources, combined with the aforementioned advances in the field of operations research, enable new modes of study and analysis in healthcare. In this thesis, we take a data-driven approach to decision-making in healthcare through the lenses of optimization, statistics, and machine learning. In Parts I and II of the thesis, we apply mixed-integer optimization to enhance the design and analysis of clinical trials, a critical step in the approval process for innovative medical treatments. In Part I, we present a robust mixed-integer optimization algorithm for allocating subjects to treatment groups in sequential experiments. By improving covariate balance across treatment groups, the proposed method yields statistical power at least as high as, and sometimes significantly higher than, state-of- the-art covariate-adaptive randomization approaches. In Part II, we present a mixed-integer optimization approach for identifying exceptional responders in randomized trial data. In practice, this approach can be used to extract added value from costly clinical trials that may have failed to identify a positive treatment effect for the general study population, but could be beneficial to a subgroup of the population. In Part III, we present a personalized approach to diabetes management using electronic medical records. The approach is based on a k-nearest neighbors algorithm. By harnessing the power of optimization and machine learning, we can improve patient outcomes and move from the one-size-fits-all approach that dominates the medical landscape today, to a personalized, patient-centered approach.en_US
dc.description.statementofresponsibilityby Alexander Michael Weinstein.en_US
dc.format.extent110 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.titleFrom data to decisions in healthcare : an optimization perspectiveen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center.en_US
dc.identifier.oclc1006887540en_US


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