Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District.

Journal: Studies in health technology and informatics
Published Date: Jun 6, 2022

Abstract

This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM.

Authors

  • Jingxiang Zhang
    Centre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
  • Siyu Qian
    Centre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
  • Guoxin Su
    School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
  • Chao Deng
    School of Mechanical Science & Engineering, Huazhong University Of Science & Technology, 1037 Luoyu Road, Wuhan, China. Electronic address: dengchao@hust.edu.cn.
  • Ping Yu
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, the Chinese Academy of Sciences (CAS), Beijing 100190, China.

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