title: Concept negation in free text components of vaccine safety reports.
abstract: Large amounts of information are locked up in free text components of clinical reports. Surveillance systems that monitor adverse events following immunizations (AEFI) can utilize these components after concept extraction using natural language processing (NLP). Specifically, our method for the identification and filtering of negated concepts using the Unified Medical Language System (UMLS) potentially improves the quality of AEFI surveillance systems.
authors.affiliation: Public Health Informatics Fellowship Program, Career Development Division, Office of Workforce and Career Development, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
authors.forename: Herman
authors.initials: H
authors.lastname: Tolentino
authors.forename: Michael
authors.initials: M
authors.lastname: Matters
authors.forename: Wikke
authors.initials: W
authors.lastname: Walop
authors.forename: Barbara
authors.initials: B
authors.lastname: Law
authors.forename: Wesley
authors.initials: W
authors.lastname: Tong
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
authors.forename: Paul
authors.initials: P
authors.lastname: Fontelo
authors.forename: Katrin
authors.initials: K
authors.lastname: Kohl
authors.forename: Daniel
authors.initials: D
authors.lastname: Payne
mesh_terms: D000465:Algorithms; D006801:Humans; D009323:Natural Language Processing; D012449:Safety; D017432:Unified Medical Language System; D014612:Vaccines / Q000009:adverse effects*
chemical_list: D014612:Vaccines