Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.

Journal: BMC medical informatics and decision making
PMID: 39563368

Abstract

BACKGROUND: Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes.

Authors

  • Ha Na Cho
    Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Imjin Ahn
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hansle Gwon
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hee Jun Kang
    Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olumpicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
  • Yunha Kim
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hyeram Seo
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Heejung Choi
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Minkyoung Kim
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Jiye Han
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Gaeun Kee
    Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, 05505, Seoul, Songpagu, Korea.
  • Seohyun Park
    Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea.
  • Tae Joon Jun
  • Young-Hak Kim
    Asan Medical Center, Seoul, Republic of Korea.

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