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Natural Language Processing for Asthma Ascertainment in Different Practice Settings.

Chung-Il Wi1, Sunghwan Sohn2, Mir Ali3

  • 1Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Asthma Epidemiology Research Unit, Mayo Clinic, Rochester, Minn.

The Journal of Allergy and Clinical Immunology. in Practice
|June 22, 2017
PubMed
Summary
This summary is machine-generated.

This study adapted a natural language processing algorithm (NLP-PAC) for asthma diagnosis using electronic health records. The adapted algorithm showed high accuracy in a new healthcare setting, demonstrating its potential for large-scale asthma research.

Keywords:
Algorithm adaptabilityAsthma ascertainmentElectronic health recordsEpidemiologyInformaticsNatural language processingRetrospective studyValidation

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Area of Science:

  • Medical Informatics
  • Clinical Research
  • Artificial Intelligence in Healthcare

Background:

  • A natural language processing (NLP) algorithm, NLP-PAC, was developed for asthma ascertainment using electronic health records (EHRs) at Mayo Clinic.
  • Asthma ascertainment from EHRs is crucial for research and clinical care.

Purpose of the Study:

  • To assess the external validity of the NLP-PAC algorithm by adapting it to a different healthcare setting, Sanford Children Hospital.
  • To evaluate the performance of the adapted NLP-PAC algorithm for asthma diagnosis.

Main Methods:

  • A retrospective cohort study of the 2011-2012 Sanford Birth cohort (n=595) was conducted.
  • Manual chart review was used for asthma ascertainment based on predetermined asthma criteria (PAC).
  • The cohort was split into training (n=298) and testing (n=297) sets to evaluate the adapted NLP-PAC algorithm.

Main Results:

  • The adapted NLP-PAC algorithm achieved high performance metrics in the test cohort (n=297): 92% sensitivity, 96% specificity, 89% positive predictive value, and 97% negative predictive value.
  • The algorithm identified 25% of subjects with asthma, closely matching human abstractors (24%).
  • Asthma risk factors identified by the NLP algorithm were consistent with manual chart review findings.

Conclusions:

  • The NLP-PAC algorithm was successfully implemented and validated in a second healthcare setting, demonstrating its external validity.
  • Automated asthma ascertainment using EHR data with NLP-PAC is feasible and has the potential to facilitate large-scale, multisite asthma studies.
  • This approach can significantly improve asthma care and research by enabling efficient data analysis.