{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:36:50Z","timestamp":1740202610565,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Social media sites, such as Twitter, are a rich source of many kinds of information, including health-related information. Accurate detection of entities such as diseases, drugs, and symptoms could be used for biosurveillance (e.g. monitoring of flu) and identification of adverse drug events. However, a critical assessment of performance of current text mining technology on Twitter has not been done yet in the medical domain. Here, we study the development of a Twitter data set annotated with relevant medical entities which we have publicly released. The manual annotation results show that it is possible to perform high-quality annotation despite of the complexity of medical terminology and the lack of context in a tweet. Furthermore, we have evaluated the capability of state-of-the-art approaches to reproduce the annotations in the data set. The best methods achieve F-scores of 55&amp;ndash;66%. The data analysis and the preliminary results provide valuable insights on identifying medical entities in Twitter for various applications.<\/jats:p>","DOI":"10.3233\/978-1-61499-564-7-643","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:13:02Z","timestamp":1740157982000},"source":"Crossref","is-referenced-by-count":0,"title":["Identifying Diseases, Drugs, and Symptoms in Twitter"],"prefix":"10.3233","author":[{"family":"Jimeno-Yepes Antonio","sequence":"additional","affiliation":[]},{"family":"MacKinlay Andrew","sequence":"additional","affiliation":[]},{"family":"Han Bo","sequence":"additional","affiliation":[]},{"family":"Chen Qiang","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2015: eHealth-enabled Health"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:54:13Z","timestamp":1740160453000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-563-0&spage=643&doi=10.3233\/978-1-61499-564-7-643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-564-7-643","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}