TY - JOUR
T1 - Application of a natural language processing algorithm to asthma ascertainment
T2 - An automated chart review
AU - Wi, Chung Il
AU - Sohn, Sunghwan
AU - Rolfes, Mary C.
AU - Seabright, Alicia
AU - Ryu, Euijung
AU - Voge, Gretchen
AU - Bachman, Kay A.
AU - Park, Miguel A.
AU - Kita, Hirohito
AU - Croghan, Ivana T.
AU - Liu, Hongfang
AU - Juhn, Young J.
N1 - Publisher Copyright:
Copyright © 2017 by the American Thoracic Society.
PY - 2017/8/15
Y1 - 2017/8/15
N2 - Rationale: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. Objectives: 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). Methods: 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). Measurements and Main Results: 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. Conclusions: 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.
AB - Rationale: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. Objectives: 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). Methods: 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). Measurements and Main Results: 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. Conclusions: 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.
KW - Electronic medical records
KW - Informatics
KW - Retrospective study
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U2 - 10.1164/rccm.201610-2006OC
DO - 10.1164/rccm.201610-2006OC
M3 - Article
C2 - 28375665
AN - SCOPUS:85028622979
SN - 1073-449X
VL - 196
SP - 430
EP - 437
JO - American journal of respiratory and critical care medicine
JF - American journal of respiratory and critical care medicine
IS - 4
ER -