Unsupervised method for automatic construction of a disease dictionary from a large free text collection.
Concept specific lexicons (e.g. diseases, drugs, anatomy) are a critical source of background knowledge for many medical language-processing systems. However, the rapid pace of biomedical research and the lack of constraints on usage ensure that such dictionaries are incomplete. Focusing on disease terminology, we have developed an automated, unsupervised, iterative pattern learning approach for constructing a comprehensive medical dictionary of disease terms from randomized clinical trial (RCT) abstracts, and we compared different ranking methods for automatically extracting con-textual patterns and concept terms. When used to identify disease concepts from 100 randomly chosen, manually annotated clinical abstracts, our disease dictionary shows significant performance improvement (F1 increased by 35-88%) over available, manually created disease terminologies.
Xu
Rong
R
Center for Biomedical Informatics Research, Stanford University, CA, USA. xurong@stanford.edu
Supekar
Kaustubh
K
Morgan
Alex
A
Das
Amar
A
Garber
Alan
A
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D004018:Dictionaries, Medical as Topic; D004194:Disease / Q000145:classification*; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D016032:Randomized Controlled Trials as Topic; D013358:Subject Headings; D009626:Terminology as Topic