title: Mining FDA drug labels for medical conditions.
abstract: BACKGROUND
  Cincinnati Children's Hospital Medical Center (CCHMC) has built the initial Natural Language Processing (NLP) component to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration's (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task.
  
  
  METHODS
  This paper discusses a hybrid NLP system, called AutoMCExtractor, to collect medical conditions (including disease/disorder and sign/symptom) from drug labels published by the FDA. Altogether, 6,611 medical conditions in a manually-annotated gold standard were used for the system evaluation. The pre-processing step extracted the plain text from XML file and detected eight related LOINC sections (e.g. Adverse Reactions, Warnings and Precautions) for medical condition extraction. Conditional Random Fields (CRF) classifiers, trained on token, linguistic, and semantic features, were then used for medical condition extraction. Lastly, dictionary-based post-processing corrected boundary-detection errors of the CRF step. We evaluated the AutoMCExtractor on manually-annotated FDA drug labels and report the results on both token and span levels.
  
  
  RESULTS
  Precision, recall, and F-measure were 0.90, 0.81, and 0.85, respectively, for the span level exact match; for the token-level evaluation, precision, recall, and F-measure were 0.92, 0.73, and 0.82, respectively.
  
  
  CONCLUSIONS
  The results demonstrate that (1) medical conditions can be extracted from FDA drug labels with high performance; and (2) it is feasible to develop a framework for an intelligent database system.
authors.affiliation: Division of Biomedical Informatics, Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.forename: Louise
authors.initials: L
authors.lastname: Deleger
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.forename: Haijun
authors.initials: H
authors.lastname: Zhai
authors.forename: Megan
authors.initials: M
authors.lastname: Kaiser
authors.forename: Laura
authors.initials: L
authors.lastname: Stoutenborough
authors.forename: Anil G
authors.initials: AG
authors.lastname: Jegga
authors.forename: Kevin Bretonnel
authors.initials: KB
authors.lastname: Cohen
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D057225:Data Mining / Q000379:methods*; D004348:Drug Labeling; D006801:Humans; D008509:Medication Systems; D009323:Natural Language Processing; D009820:Ohio; D014481:United States; D014486:United States Food and Drug Administration