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Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study

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  • Yao Tong

    (School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
    Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047, Seattle, WA 98109, USA
    Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
    School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Beilei Lin

    (School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China)

  • Gang Chen

    (Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
    School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Zhenxiang Zhang

    (School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China)

Abstract

Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use of a generalizable predictive methodology to determine the COC in patients has been underinvestigated. To fill this research gap, this study aimed to develop a machine learning model to predict the future COC of asthma patients and explore the associated factors. We included 31,724 adult outpatients with asthma who received care from the University of Washington Medicine between 2011 and 2018, and examined 138 features to build the machine learning model. Following the 10-fold cross-validations, the proposed model yielded an accuracy of 88.20%, an average area under the receiver operating characteristic curve of 0.96, and an average F1 score of 0.86. Further analysis revealed that the severity of asthma, comorbidities, insurance, and age were highly correlated with the COC of patients with asthma. This study used predictive methods to obtain the COC of patients, and our excellent modeling strategy achieved high performance. After further optimization, the model could facilitate future clinical decisions, hospital management, and improve outcomes.

Suggested Citation

  • Yao Tong & Beilei Lin & Gang Chen & Zhenxiang Zhang, 2022. "Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study," IJERPH, MDPI, vol. 19(3), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1237-:d:731057
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    References listed on IDEAS

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    1. Hamed Asadi & Richard Dowling & Bernard Yan & Peter Mitchell, 2014. "Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
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