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Prediction model of COPD acute exacerbation with big data by machine learning methods

Chin Kook Rhee, Jin Woo Kim, Kwang Ha Yoo, Ki-Suck Jung
European Respiratory Journal 2020 56: 4911; DOI: 10.1183/13993003.congress-2020.4911
Chin Kook Rhee
1Seoul St Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
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  • For correspondence: chinkook77@gmail.com
Jin Woo Kim
2Uijeongbu St Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
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Kwang Ha Yoo
3Konkuk University School of Medicine, Seoul, Republic of Korea
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Ki-Suck Jung
4Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
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Abstract

Introduction: There has been little study regarding prediction of acute exacerbation.

Aims and Objective: This study aimed to develop prediction model of COPD acute exacerbation with big data by machine learning methods.

Methods: We investigated data from 594 COPD patients who were enrolled in the KOCOSS cohort. Smoking status, lung function, body mass index, and COPD assessment test (CAT) score were collected from cohort data. We merged patients’ information with the Korean Health Insurance database. Comorbidity, health care utilization, moderate to severe exacerbation, and COPD medications between 2008 and 2012 were collected. We also collected daily air pollution level, temperature, humidity, and wind velocity. Data on the activities of respiratory viruses were collected. Prediction model was developed by deep learning methods and also statistical method. Deep neural network (DNN), random forest, and generalized estimating equation (GEE) were utilized.

Results: Area under the curve (AUC) value for prediction of acute exacerbation was highest by random forest method (0.8722) followed by GEE (0.8617) and DNN (0.8493). Sensitivity was highest by random forest (0.8613) followed by DNN (0.8000) and GEE (0.7613). In GEE analysis, female, CAT score, FEV1 (%), number of exacerbations in previous one year, use of bronchodilator, history of asthma, influenza, and human coronavirus were significantly associated with acute exacerbation.

Conclusions: Prediction model for acute exacerbation of COPD was developed with big data by machine learning methods. AUC and sensitivity were higher in model by machine learning methods compared with GEE.

  • COPD - exacerbations

Footnotes

Cite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4911.

This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2020
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Prediction model of COPD acute exacerbation with big data by machine learning methods
Chin Kook Rhee, Jin Woo Kim, Kwang Ha Yoo, Ki-Suck Jung
European Respiratory Journal Sep 2020, 56 (suppl 64) 4911; DOI: 10.1183/13993003.congress-2020.4911

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Prediction model of COPD acute exacerbation with big data by machine learning methods
Chin Kook Rhee, Jin Woo Kim, Kwang Ha Yoo, Ki-Suck Jung
European Respiratory Journal Sep 2020, 56 (suppl 64) 4911; DOI: 10.1183/13993003.congress-2020.4911
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