ArticleBMC pulmonary medicine2023
Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method.
Article in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning.The Journal of international medical research · 2026Article
- Exacerbation-related burden in patients with severe asthma in South Korea: a population-based cohort study.BMJ open respiratory research · 2026Article
- Postnatal antibiotic exposure due to maternal group B streptococcus is associated with childhood asthma.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Article
- Fungal Microbiome Diversity in Urban Forest Decreases Asthma and Allergic Inflammation.Allergy, asthma & immunology research · 2025Article
- Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.Allergy, asthma & immunology research · 2024Article
- Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models.Journal of asthma and allergy · 2024Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
introductionAnalysis of the National Health Insurance data has been actively carried out for the purpose of academic research and establishing scientific evidences for health care service policy in asthma. However, there has been a limitation for the accuracy of the data extracted through conventional operational definition. In this study, we verified the accuracy of conventional operational definition of asthma, by applying it to a real hospital setting. And by using a machine learning technique, we established an appropriate operational definition that predicts asthma more accurately.
methodsWe extracted asthma patients using the conventional operational definition of asthma at Seoul St. Mary's hospital and St. Paul's hospital at the Catholic University of Korea between January 2017 and January 2018. Among these extracted patients of asthma, 10% of patients were randomly sampled. We verified the accuracy of the conventional operational definition for asthma by matching actual diagnosis through medical chart review. And then we operated machine learning approaches to predict asthma more accurately.
resultsA total of 4,235 patients with asthma were identified using a conventional asthma definition during the study period. Of these, 353 patients were collected. The patients of asthma were 56% of study population, 44% of patients were not asthma. The use of machine learning techniques improved the overall accuracy. The XGBoost prediction model for asthma diagnosis showed an accuracy of 87.1%, an AUC of 93.0%, sensitivity of 82.5%, and specificity of 97.9%. Major explanatory variable were ICS/LABA,LAMA and LTRA for proper diagnosis of asthma.
conclusionsThe conventional operational definition of asthma has limitation to extract true asthma patients in real world. Therefore, it is necessary to establish an accurate standardized operational definition of asthma. In this study, machine learning approach could be a good option for building a relevant operational definition in research using claims data.
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