Identifying Drug Related Events from Social Media

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dc.contributor.authorNoh, Jeonghoen
dc.contributor.authorKim, Sunghoen
dc.contributor.authorYou, Jisuen
dc.contributor.authorYoonju, Leeen
dc.contributor.authorKye, Woojinen
dc.date.accessioned2017-05-13T12:39:15Zen
dc.date.available2017-05-13T12:39:15Zen
dc.date.issued2017-05-10en
dc.description.abstractThe overall goal of the project was to establish an innovative information system, which can automatically detect and extract content related to side effect of drugs from user reviews, determine whether they are talking about effectiveness or adverse drug events, extract keywords or phrases related to effectiveness or adverse drug events, and visualize the resulting information to doctors and patients. Our group was provided with crawled Twitter reviews and social network forum reviews on drugs that are used to treat diabetes. The raw data were manually labeled in four different label for named entity recognition in order to create training, testing, and validation sets. Using the training data set, a side effect dictionary was created using PamTAT. Side effect dictionary was then refined by removing neutral words to increase accuracy. To validate the accuracy of the generated side effect dictionary, the results of side effect analysis based on the generated dictionary and two other general negative word dictionaries were compared. The generated side effect dictionary performed better in recognizing side effect entities. After validation, the generated dictionary was further tested with a set of user reviews on a drug that is used to treat stroke. Using generated dictionary, the project accomplished to accurately determine if any reviews relates to the mention of side effect of specific drugs. The project successfully delivered to accurately detect mention of side effect from the reviews in > 90% accuracy. Resulting algorithm can be used to create innovative information system to detect and extract content related to side effect of drugs for any other drugs with creation of problem specific dictionary. The project should be further developed to incorporate automatic extraction of user reviews, analysis of data, and visualization of results.en
dc.description.notesDrugEventsSocialMediaReport.docx - Presentation file in docx format DrugEventsSocialMediaReport.pdf - Presentation file in pdf format DrugEventsSocialMediaPresentation.pptx - Presentation file in pptx format DrugEventsSocialMediaPresentation.pdf - Presentation file in pdf format PamTaT4VTWorks - CSV and XML files to use PamTATen
dc.identifier.urihttp://hdl.handle.net/10919/77628en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMachine learningen
dc.subjectpamTaten
dc.subjectmultimediaen
dc.subjecthypertexten
dc.subjectweb crawlingen
dc.subjectconfusion matrixen
dc.subjectsmoke listen
dc.subjectstatistical learningen
dc.subjectstatistical modelen
dc.titleIdentifying Drug Related Events from Social Mediaen
dc.typeDataseten
dc.typePresentationen
dc.typeReporten
dc.typeSoftwareen

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CS4624: Multimedia, Hypertext, and Information Access