AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the exploration of AI-based predictive models as a means to enhance predictive accuracy and, consequently, patient outcomes.

Authors

  • Shixin Yuan
    School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
  • Zihuan Yang
    School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
  • Junjie Li
    Department of Emergency, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, China.
  • Changde Wu
    School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China. njwu163@163.com.
  • Songqiao Liu
    School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China. liusongqiao@ymail.com.