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Am J Respir Crit Care Med. 2017 Aug 15;196(4):430-437. doi: 10.1164/rccm.201610-2006OC.

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

Author information

1
1 Department of Pediatric and Adolescent Medicine.
2
2 Asthma Epidemiology Research Unit.
3
3 Division of Biomedical Statistics and Informatics, and.
4
4 Mayo Medical School, Rochester, Minnesota.
5
5 Division of Neonatology, Children's Hospitals and Clinics of Minnesota, Minneapolis, Minnesota; and.
6
6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
7
7 Department of Medicine Research, Mayo Clinic, Rochester, Minnesota.

Abstract

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.

KEYWORDS:

electronic medical records; informatics; retrospective study

Comment in

PMID:
28375665
PMCID:
PMC5564673
DOI:
10.1164/rccm.201610-2006OC
[Indexed for MEDLINE]
Free PMC Article

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