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Therefore, this work focusses on theadaptation of a machine learning\u2010based system for the identificationand extraction of potential adverse drug event relations from MEDLINE casereports. It relies on a high quality corpus that was manually annotatedusing an ontology\u2010driven methodology. Qualitative evaluation of thesystem showed robust results. An experiment with large scale relationextraction from MEDLINE delivered under\u2010identified potential adversedrug events not reported in drug monographs. Overall, this approach providesa scalable auto\u2010assistance platform for drug safety professionals toautomatically collect potential adverse drug events communicated asfree\u2010text data.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/2041-1480-3-15","type":"journal-article","created":{"date-parts":[[2013,3,13]],"date-time":"2013-03-13T19:15:06Z","timestamp":1363202106000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":136,"title":["Extraction of potential adverse drug events from medical case reports"],"prefix":"10.1186","volume":"3","author":[{"given":"Harsha","family":"Gurulingappa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul","family":"Mateen\u2010Rajpu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Toldo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,12,20]]},"reference":[{"issue":"7\u20108","key":"107_CR1","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.drudis.2008.12.012","volume":"14","author":"M Hauben","year":"2009","unstructured":"Hauben M, Bate A: Decision support methods for the detection of adverse events inpost\u2010marketing data. 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