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

Autor: Euijung Ryu, Young J. Juhn, Hirohito Kita, Hongfang Liu, Sunghwan Sohn, Gretchen A. Voge, Mary C. Rolfes, Chung Il Wi, Miguel A. Park, Alicia Seabright, Ivana T. Croghan, Kay Bachman
Rok vydání: 2017
Předmět:
Male
Pulmonary and Respiratory Medicine
Pediatrics
medicine.medical_specialty
Adolescent
Minnesota
Critical Care and Intensive Care Medicine
computer.software_genre
Sensitivity and Specificity
Cohort Studies
03 medical and health sciences
0302 clinical medicine
Risk Factors
immune system diseases
Chart review
Prevalence
Criterion validity
medicine
Electronic Health Records
Humans
030212 general & internal medicine
Child
Retrospective Studies
Natural Language Processing
Asthma
business.industry
Medical record
Gold standard
Reproducibility of Results
Construct validity
Retrospective cohort study
Original Articles
medicine.disease
respiratory tract diseases
030228 respiratory system
Child
Preschool
Informatics
Female
Artificial intelligence
business
Algorithm
computer
Natural language processing
Zdroj: American Journal of Respiratory and Critical Care Medicine. 196:430-437
ISSN: 1535-4970
1073-449X
Popis: 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.
Databáze: OpenAIRE
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