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

• Published in: American journal of respiratory and critical care medicine Vol. 196; no. 4; pp. 430 - 437
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: United States American Thoracic Society 15.08.2017
• 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; retrospective study; electronic medical records; informatics
• ISSN: 1073-449X; 1535-4970; 1535-4970
• DOI: 10.1164/rccm.201610-2006OC

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.