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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automated chart review utilizing natural language processing algorithm for asthma predictive index.

Harsheen Kaur1,2,3, Sunghwan Sohn4, Chung-Il Wi1,2

  • 1Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA.

BMC Pulmonary Medicine
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Summary
This summary is machine-generated.

A new natural language processing (NLP) algorithm, NLP-API, can accurately identify children meeting Asthma Predictive Index (API) criteria in electronic health records (EHR). This tool aids asthma research and patient care by automating patient identification.

Keywords:
APIAsthmaEpidemiologyInformaticsNLP

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Pediatric Asthma Research

Background:

  • Electronic health records (EHR) lack automated methods for identifying patients meeting Asthma Predictive Index (API) criteria.
  • Developing such an algorithm is crucial for efficient asthma research and clinical management.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) algorithm, termed NLP-API, for automatically extracting patients who meet API criteria from EHR data.
  • To assess the accuracy and construct validity of the NLP-API algorithm.

Main Methods:

  • A cross-sectional study nested within a birth cohort was conducted.
  • The NLP-API algorithm was trained on a cohort of 87 patients and validated on a separate cohort of 427 patients.
  • Criterion validity was assessed using sensitivity, specificity, positive predictive value, and negative predictive value against manual chart review.

Main Results:

  • The NLP-API algorithm demonstrated high accuracy in identifying asthma status, with a sensitivity of 86% and specificity of 98%.
  • Positive predictive value was 88% and negative predictive value was 98%.
  • Asthma status identified by NLP-API was significantly associated with known risk factors, including allergic rhinitis, eczema, family history of asthma, and maternal smoking during pregnancy.

Conclusions:

  • The NLP-API algorithm effectively ascertains asthma status in children by analyzing EHR data.
  • This NLP tool has the potential to improve asthma care and facilitate large-scale research by enabling efficient population management and identification of children meeting API criteria.