2026-09-19T11:16:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664372022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stephanie Seneff.
Li, Yueyang Alice
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:25:56Z
2011-10-17T21:25:56Z
2011
2011
http://hdl.handle.net/1721.1/66437
755631510
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 85-92).
We 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.
by Yueyang Alice Li.
M.Eng.
92 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Medical data mining : improving information accessibility using online patient drug reviews
Improving information accessibility using online patient drug reviews
Thesis
application/pdf
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Publication
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<Abstract>We 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.</Abstract>
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