Concept negation in free text components of vaccine safety reports.
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.
Tolentino
Herman
H
Public Health Informatics Fellowship Program, Career Development Division, Office of Workforce and Career Development, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Matters
Michael
M
Walop
Wikke
W
Law
Barbara
B
Tong
Wesley
W
Liu
Fang
F
Fontelo
Paul
P
Kohl
Katrin
K
Payne
Daniel
D
D000465:Algorithms; D006801:Humans; D009323:Natural Language Processing; D012449:Safety; D017432:Unified Medical Language System; D014612:Vaccines / Q000009:adverse effects*
D014612:Vaccines