Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

Journal: BMC public health
PMID: 38965513

Abstract

BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI).

Authors

  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Yonghong Zhang
    Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
  • Shuping Nie
    Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518003, PR China.
  • Jie Ning
    Department of Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei 230022, China.
  • Qinjin Wang
    Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518003, PR China.
  • Hanmei Yuan
    Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518003, PR China.
  • Hui Wu
    China Medical University College of Health Management, Shenyang 110122, Liaoning Province, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Wenbiao Hu
    School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. w2.hu@qut.edu.au.
  • Chao Wu

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