Identifying Diseases, Drugs, and Symptoms in Twitter.
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 55-66%. The data analysis and the preliminary results provide valuable insights on identifying medical entities in Twitter for various applications.
Melbourne Research Lab, IBM Research, Victoria, Australia.
Antonio
A
Jimeno-Yepes
Melbourne Research Lab, IBM Research, Victoria, Australia.
Andrew
A
MacKinlay
Melbourne Research Lab, IBM Research, Victoria, Australia.
Bo
B
Han
Melbourne Research Lab, IBM Research, Victoria, Australia.
Qiang
Q
Chen
D057225:Data Mining / Q000379:methods*; D004194:Disease / Q000145:classification*; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations / Q000145:classification*; D011159:Population Surveillance / Q000379:methods; D061108:Social Media / Q000145:classification*; D063189:Symptom Assessment / Q000145:classification*; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
D004364:Pharmaceutical Preparations