title: Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.
abstract: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.
  
  
  
  We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).
  
  
  
  The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis).
  
  
  
  After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.
  
  
  
  Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.
authors.affiliation: 1 Department of Pediatric and Adolescent Medicine.|2 Asthma Epidemiology Research Unit.
authors.forename: Chung-Il
authors.initials: CI
authors.lastname: Wi
authors.affiliation: 3 Division of Biomedical Statistics and Informatics, and.
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Sohn
authors.affiliation: 2 Asthma Epidemiology Research Unit.|4 Mayo Medical School, Rochester, Minnesota.
authors.forename: Mary C
authors.initials: MC
authors.lastname: Rolfes
authors.affiliation: 2 Asthma Epidemiology Research Unit.
authors.forename: Alicia
authors.initials: A
authors.lastname: Seabright
authors.affiliation: 3 Division of Biomedical Statistics and Informatics, and.
authors.forename: Euijung
authors.initials: E
authors.lastname: Ryu
authors.affiliation: 1 Department of Pediatric and Adolescent Medicine.|2 Asthma Epidemiology Research Unit.|5 Division of Neonatology, Children's Hospitals and Clinics of Minnesota, Minneapolis, Minnesota; and.
authors.forename: Gretchen
authors.initials: G
authors.lastname: Voge
authors.affiliation: 6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
authors.forename: Kay A
authors.initials: KA
authors.lastname: Bachman
authors.affiliation: 6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Park
authors.affiliation: 6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
authors.forename: Hirohito
authors.initials: H
authors.lastname: Kita
authors.affiliation: 7 Department of Medicine Research, Mayo Clinic, Rochester, Minnesota.
authors.forename: Ivana T
authors.initials: IT
authors.lastname: Croghan
authors.affiliation: 3 Division of Biomedical Statistics and Informatics, and.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
authors.affiliation: 1 Department of Pediatric and Adolescent Medicine.|2 Asthma Epidemiology Research Unit.
authors.forename: Young J
authors.initials: YJ
authors.lastname: Juhn
mesh_terms: D000293:Adolescent; D001249:Asthma / Q000453:epidemiology*; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008910:Minnesota / Q000453:epidemiology; D009323:Natural Language Processing; D015995:Prevalence; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity
keywords: electronic medical records; informatics; retrospective study