Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections.

Journal: Frontiers in pediatrics
Published Date: Dec 11, 2024

Abstract

BACKGROUND: Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.

Authors

  • Yuwen Liu
    Department of Orthopaedic Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China.
  • Yuhan Wu
    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Wei Hu
    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Guixin Sun
    Department of Traumatic Surgery, Shanghai East Hospital, Nanjing Medical University, Nanjing, China.
  • Pengfei Zheng
    Department of Orthopaedic Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China.

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