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Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review

American journal of respiratory and critical care medicine, 2017-08, Vol.196 (4), p.430-437 [Peer Reviewed Journal]

Copyright American Thoracic Society Aug 15, 2017 ;Copyright © 2017 by the American Thoracic Society 2017 ;ISSN: 1073-449X ;EISSN: 1535-4970 ;DOI: 10.1164/rccm.201610-2006oc ;PMID: 28375665

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Title:
Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review
Author: Wi, Chung-Il ; Sohn, Sunghwan ; Rolfes, Mary C ; Seabright, Alicia ; Ryu, Euijung ; Voge, Gretchen ; Bachman, Kay A ; Park, Miguel A ; Kita, Hirohito ; Croghan, Ivana T ; Liu, Hongfang ; Juhn, Young J
Subjects: Adolescent ; Algorithms ; Allergies ; Asthma ; Asthma - epidemiology ; Automation ; Child ; Child, Preschool ; Cohort Studies ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Epidemiology ; Female ; Health risk assessment ; Humans ; Male ; Medical records ; Minnesota - epidemiology ; Natural Language Processing ; Original ; Pediatrics ; Prevalence ; Primary care ; Reproducibility of Results ; Retrospective Studies ; Rheumatoid arthritis ; Risk Factors ; Sensitivity and Specificity ; Validation studies ; Validity
Is Part Of: American journal of respiratory and critical care medicine, 2017-08, Vol.196 (4), p.430-437
Description: 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.
Publisher: United States: American Thoracic Society
Language: English
Identifier: ISSN: 1073-449X
EISSN: 1535-4970
DOI: 10.1164/rccm.201610-2006oc
PMID: 28375665
Source: GFMER Free Medical Journals
MEDLINE
Alma/SFX Local Collection
ProQuest Central

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Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review

Copyright American Thoracic Society Aug 15, 2017 ;Copyright © 2017 by the American Thoracic Society 2017 ;ISSN: 1073-449X ;EISSN: 1535-4970 ;DOI: 10.1164/rccm.201610-2006oc ;PMID: 28375665

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