Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

Journal: Journal of medical Internet research
PMID: 38498038

Abstract

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice.

Authors

  • Meicheng Yang
    The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Wenhan Hu
    Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Massimo Mischi
    Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Caifeng Shan
  • Jianqing Li
    School of Instrument Science and Engineering, Southeast University, Sipailou 2, Nanjing 210096, China.
  • Xi Long
    1Department of Electrical EngineeringEindhoven University of Technology5612AZEindhovenThe Netherlands.
  • Chengyu Liu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.

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