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Identifying Asthma-Related Symptoms From Electronic Health Records Using a Hybrid Natural Language Processing

Fagen Xie1, Robert S Zeiger2,3, Mary Marycania Saparudin1

  • 1Department of Research and Evaluation, Kaiser Permanente South California, 100 S Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, United States, 1 626-564-3294.

JMIR AI
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Summary

A new hybrid natural language processing (NLP) algorithm effectively identifies asthma symptoms in clinical notes. This tool aids in early asthma detection and predicting exacerbation risk using unstructured health data.

Keywords:
asthmaelectronic health recordnatural language processingrule-based algorithmsymptom extractiontransformer-based algorithm

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Area of Science:

  • Computational linguistics
  • Health informatics
  • Respiratory medicine

Background:

  • Asthma exacerbations are often predicted by symptoms documented in unstructured clinical notes.
  • Current methods for extracting these symptoms from free-text data are insufficient.

Purpose of the Study:

  • To develop a natural language processing (NLP) algorithm for identifying asthma-related symptoms from clinical notes.
  • To improve the capture of crucial symptom data within a large healthcare system.

Main Methods:

  • A hybrid NLP algorithm was created by combining rule-based and transformer-based deep learning models.
  • The algorithm was trained and refined using manually annotated clinical notes from 2013-2018 and 2021-2022.
  • Analysis involved over 11 million clinical notes to identify four common asthma symptoms.

Main Results:

  • The hybrid NLP algorithm identified at least one asthma symptom in 7.55% of notes.
  • Cough was the most frequently identified symptom (5.81% of notes).
  • The algorithm demonstrated high performance with positive predictive values >96% and F1-scores >0.95.

Conclusions:

  • The developed NLP algorithms effectively extract asthma symptoms from unstructured clinical notes.
  • These tools can support earlier asthma diagnosis and risk prediction for exacerbations.