Unsupervised method for extracting machine understandable medical knowledge from a large free text collection
View/ Open
Unsupervised Method for Extracting.pdf (345.4Kb)
Access Status
Full text of the requested work is not available in DASH at this time ("restricted access"). For more information on restricted deposits, see our FAQ.Published Version
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815389/Metadata
Show full item recordCitation
Xu Rong, Amar K. Das, and Alan M. Garber. 2009. Unsupervised method for extracting machine understandable medical knowledge from a large free text collection. AMIA Annual Symposium Proceedings 709-713.Abstract
Definitions of medical concepts (e.g diseases, drugs) are essential background knowledge for researchers, clinicians and health care consumers. However, the rapid growth of biomedical research requires that such knowledge continually needs updating. To address this problem, we have developed an unsupervised pattern learning approach that extracts disease and drug definitions from automatically structured randomized clinical trial (RCT) abstracts. In addition, each extracted definition is semantically classified without relying on external medical knowledge. When used to identify definitions from 100 manually annotated RCT abstracts, our medical definition knowledge base has precision of 0.97, recall of 0.93, F1 of 0.94 and semantic classification accuracy of 0.96.Citable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:11577723
Collections
- HCA Scholarly Articles [622]
Contact administrator regarding this item (to report mistakes or request changes)