{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:36:23Z","timestamp":1775054183831,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2014,2,24]],"date-time":"2014-02-24T00:00:00Z","timestamp":1393200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2014,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Method<\/jats:title>\n            <jats:p>We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1472-6947-14-13","type":"journal-article","created":{"date-parts":[[2014,2,24]],"date-time":"2014-02-24T06:01:28Z","timestamp":1393221688000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["A pipeline to extract drug-adverse event pairs from multiple data sources"],"prefix":"10.1186","volume":"14","author":[{"given":"SriJyothsna","family":"Yeleswarapu","sequence":"first","affiliation":[]},{"given":"Aditya","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Joseph","sequence":"additional","affiliation":[]},{"given":"Vangala Govindakrishnan","family":"Saipradeep","sequence":"additional","affiliation":[]},{"given":"Rajgopal","family":"Srinivasan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,2,24]]},"reference":[{"key":"785_CR1","first-page":"328","volume":"16","author":"X Wang","year":"2009","unstructured":"Wang X, Hripcsak G, Markatou M, Friedman C: Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. 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