Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

Journal: Computer assisted surgery (Abingdon, England)
Published Date: Jun 11, 2024

Abstract

BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

Authors

  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Shengsheng Huang
    The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Chenxing Zhou
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Jichong Zhu
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Tianyou Chen
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Sitan Feng
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Chengqian Huang
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Zequn Wang
    Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Shaofeng Wu
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.
  • Chong Liu
    * Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China.
  • Xinli Zhan
    Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China.

Keywords

External Resources

Popular Topics
Recent Journals