Publications

Concept specific lexicons (e.g. diseases, drugs, anat-
omy) 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 differ-
ent 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 dic-
tionary shows significant performance improvement
(F1 increased by 35-88%) over available, manually
created disease terminologies.