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

Hyonsoo Joo; Daeun Lee; Sang Haak Lee; Young Kyoon Kim; Chin Kook Rhee

doi:10.1186/s12890-023-02479-4

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

BMC Pulm Med. 2023 Jun 6;23(1):196. doi: 10.1186/s12890-023-02479-4.

Authors

Hyonsoo Joo¹, Daeun Lee², Sang Haak Lee³, Young Kyoon Kim^#⁴, Chin Kook Rhee^#⁵

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Uijeongbu St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
² Departement of Applied Statistics, Yonsei University, Seoul, Republic of Korea.
³ Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
⁴ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpodaero, Seochogu, Seoul, 06591, Republic of Korea. youngkim@catholic.ac.kr.
⁵ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpodaero, Seochogu, Seoul, 06591, Republic of Korea. chinkook77@gmail.com.

^# Contributed equally.

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Asthma; Conventional operational definition; Machine learning.

MeSH terms

Asthma* / diagnosis
Humans
Machine Learning
Research Design
Seoul

Abstract

MeSH terms

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