title: Extracting drug indication information from structured product labels using natural language processing.
abstract: OBJECTIVE
  To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies.
  
  
  METHODS
  We used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the Semantic Medline project.
  
  
  RESULTS
  A total of 6797 drug labels were processed, resulting in 19 473 unique drug-indication pairs. Manual review of 298 most frequently prescribed drugs by seven physicians showed a recall of 0.95 and precision of 0.77. Inter-rater agreement (Fleiss κ) was 0.713. The precision of the subset of results corroborated by Semantic Medline extractions increased to 0.93.
  
  
  CONCLUSIONS
  Correlation of a patient's medical problems and drugs in an electronic health record has been used to improve data quality and reduce medication errors. Authoritative drug indication information is available from drug labels, but not in a format readily usable by computer applications. Our study shows that it is feasible to use publicly available natural language processing resources to extract drug indications from drug labels. The same method can be applied to other sections of the drug label-for example, adverse effects, contraindications.
  
  
  CONCLUSIONS
  It is feasible to use publicly available natural language processing tools to extract indication information from freely available drug labels. Named entity recognition sources (eg, MetaMap) provide reasonable recall. Combination with other data sources provides higher precision.
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, US National Institutes of Health, Bethesda, MD 20894, USA. kfung@mail.nih.gov
authors.forename: Kin Wah
authors.initials: KW
authors.lastname: Fung
authors.forename: Chiang S
authors.initials: CS
authors.lastname: Jao
authors.forename: Dina
authors.initials: D
authors.lastname: Demner-Fushman
mesh_terms: D004348:Drug Labeling; D006801:Humans; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations / Q000145:classification; D055553:Prescription Drugs; D062245:RxNorm
chemical_list: D004364:Pharmaceutical Preparations; D055553:Prescription Drugs