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dc.contributor.advisorStephanie Seneff.en_US
dc.contributor.authorLi, Yueyang Aliceen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2011-10-17T21:25:56Z
dc.date.available2011-10-17T21:25:56Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/66437
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 85-92).en_US
dc.description.abstractWe address the problem of information accessibility for patients concerned about, pharmaceutical drug side effects and experiences. We create a new corpus of online patient-provided drug reviews and present our initial experiments on that corpus. We detect biases in term distributions that show a statistically significant association between a class of cholesterol-lowering drugs called statins, and a wide range of alarming disorders, including depression, memory loss, and heart failure. We also develop an initial language model for speech recognition in the medical domain, with transcribed data on sample patient comments collected with Amazon Mechanical Turk. Our findings show that patient-reported drug experiences have great potential to empower consumers to make more informed decisions about medical drugs, and our methods will be used to increase information accessibility for consumers.en_US
dc.description.statementofresponsibilityby Yueyang Alice Li.en_US
dc.format.extent92 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleMedical data mining : improving information accessibility using online patient drug reviewsen_US
dc.title.alternativeImproving information accessibility using online patient drug reviewsen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc755631510en_US


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