A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

Journal: Critical care (London, England)
Published Date: May 28, 2024

Abstract

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored.

Authors

  • Sherali Bomrah
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No. 291, Zhongzheng Rd, Zhonghe District, New Taipei City, 235, Taiwan.
  • Mohy Uddin
    King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Executive Office, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
  • Umashankar Upadhyay
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
  • Matthieu Komorowski
    Imperial College London, London, UK.
  • Jyoti Priya
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No. 291, Zhongzheng Rd, Zhonghe District, New Taipei City, 235, Taiwan.
  • Eshita Dhar
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
  • Shih-Chang Hsu
    Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan.
  • Shabbir Syed-Abdul
    Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan;; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.

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