Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.

Journal: BMC medical informatics and decision making
Published Date: Oct 20, 2021

Abstract

BACKGROUND: Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions.

Authors

  • Yu-Tai Lo
    Department of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.).
  • Jay Chiehen Liao
    Institute of Data Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan (R.O.C.).
  • Mei-Hua Chen
    Department of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.).
  • Chia-Ming Chang
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
  • Cheng-Te Li
    Institute of Data Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan (R.O.C.). chengte@mail.ncku.edu.tw.

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