LabeledIn: Cataloging labeled indications for human drugs
A semi-automated framework to annotate drug labels for drug indications is proposed. 500 drug labels corresponding to 250 frequently accessed drugs are double-annotated. Annotation achieves 88% agreement and establishes 7805 drugdisease relationships. The results are computable, precise, and linked to the source drug labels. The resultant resource complements existing resources and is made publicly available.
drug labels drug indications corpus annotation natural language processing