Medical data mining : improving information accessibility using online patient drug reviews

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Li, Yueyang Alice

Advisor dc:contributor.advisor

• Stephanie Seneff.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/66437

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66437