Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.

ABSTRACT:

Purpose

The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma.

Methods

Data from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV₁]; PC₂₀ ≤ 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, ext

SUBMITTER: Kang N 

PROVIDER: S-EPMC10823143 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature