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Related Experiment Video

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Expert artificial intelligence-based natural language processing characterises childhood asthma.

Hee Yun Seol1,2, Mary C Rolfes3, Wi Chung1

  • 1Community Pediatrics and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA.

BMJ Open Respiratory Research
|December 29, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) using natural language processing (NLP) effectively identifies childhood asthma subgroups. This method improves asthma cohort consistency and efficiency for clinical research.

Keywords:
asthmaasthma epidemiologypaediatric asthma

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Area of Science:

  • Computational Medicine
  • Pediatric Asthma Research
  • Artificial Intelligence in Healthcare

Background:

  • Current asthma ascertainment methods lack consistency, reproducibility, and efficiency, leading to unreliable study cohorts and results.
  • This inconsistency poses significant challenges for clinical trials and epidemiological studies in asthma research.
  • There is a critical need for improved methods to accurately identify childhood asthma and its distinct subgroups.

Purpose of the Study:

  • To evaluate the effectiveness of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms in identifying childhood asthma and its subgroups.
  • To assess if NLP algorithms applied to electronic health records can systematically identify children with asthma and their unique characteristics.
  • To determine if this AI-driven approach can enhance the precision, reproducibility, and efficiency of large-scale asthma studies.

Main Methods:

  • Utilized the 1997-2007 Olmsted County Birth Cohort, comprising 8196 children.
  • Applied validated NLP algorithms for two asthma criteria: Predetermined Asthma Criteria (NLP-PAC) and Asthma Predictive Index (NLP-API).
  • Categorized subjects into four groups based on NLP-PAC and NLP-API results and characterized them; findings were validated using cluster analysis and laboratory/pulmonary function tests.

Main Results:

  • Of 8196 subjects, 20% (NLP-PAC+/NLP-API+), 12% (NLP-PAC+ only), 1% (NLP-API+ only), and 67% (NLP-PAC-/NLP-API-) were identified.
  • Children classified as NLP-PAC+/NLP-API+ exhibited earlier asthma onset, a Th2-high profile, poorer lung function, increased exacerbations, and higher comorbidity risk.
  • These AI-driven classifications were consistent with unsupervised cluster analysis and laboratory/pulmonary function test data.

Conclusions:

  • Expert AI-based NLP algorithms applied to two asthma criteria can systematically identify childhood asthma with distinct characteristics.
  • This AI-driven approach offers significant improvements in precision, reproducibility, consistency, and efficiency for large-scale asthma research.
  • The methodology holds potential for enhancing population-level asthma management strategies.