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dc.contributor.advisorMartin C. Rinard.en_US
dc.contributor.authorMcCleary, Jennifer(Jennifer A.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2021-02-19T20:58:01Z
dc.date.available2021-02-19T20:58:01Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/129921
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 67-74).en_US
dc.description.abstractPancreatic cancer is the third most lethal cancer in the U.S., causing an estimated 45,750 deaths in 2019. Of all treatments, surgical resection provides the best survival rate for pancreatic cancer. This is not feasible for the majority of pancreatic cancer patients, whose cancer is typically not diagnosed until the tumor is unresectable. Most symptoms of pancreatic cancer are typically subtle, which underscores the need for better risk modeling to predict a patient's chance of pancreatic cancer well before it would usually be diagnosed. We propose a series of novel models that apply standard machine learning techniques to Electronic Health Records (EHRs) to predict risk of pancreatic cancer in advance of cancer diagnosis. On the test dataset, two of our models achieved AUROCs of 0.85 (CI 0.81 - 0.90) and 0.79 (CI 0.77 - 0.82) with a 365-day lead time.en_US
dc.description.statementofresponsibilityby Jennifer McCleary.en_US
dc.format.extent74 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleLearning risk models for pancreatic cancer from electronic health recordsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1237530441en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2021-02-19T20:57:31Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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