Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method

• Published in: BMC pulmonary medicine Vol. 23; no. 1; p. 196
• Main Authors: Joo, Hyonsoo, Lee, Daeun, Lee, Sang Haak, Kim, Young Kyoon, Rhee, Chin Kook
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.06.2023; BioMed Central; BMC
• Subjects: Accuracy; Accuracy and precision; Asthma; Care and treatment; Chronic obstructive pulmonary disease; Conventional operational definition; Decision trees; Diagnosis; Hospitals; Inhalers; Learning algorithms; Machine learning; Medical care; Methods; Patients; Population studies; Prediction models; Pulmonology; Quality management; Regression analysis; Steroids; Variables; South Korea; Conventional operational definition; Machine learning; Asthma
• ISSN: 1471-2466; 1471-2466
• DOI: 10.1186/s12890-023-02479-4

Analysis 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. We 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. A 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. The 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.