Machine learning prediction of dropping out of outpatients with alcohol use disorders.

Journal: PloS one
Published Date: Aug 2, 2021

Abstract

BACKGROUND: Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes.

Authors

  • So Jin Park
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Sun Jung Lee
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • HyungMin Kim
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Jae Kwon Kim
    Department of Computer Science and Information Engineering, Inha University, InhaRo 100, Nam-gu, Incheon, South Korea.
  • Ji-Won Chun
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Soo-Jung Lee
    5Department of Food Science and Nutrition, Gyeongsang National University, Jinju, Gyeongnam 52828 Korea.
  • Hae Kook Lee
    Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Dai Jin Kim
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • In Young Choi
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

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