Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
Published Date: May 20, 2025

Abstract

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high mortality rates. Despite advancements in critical care, reliable early prediction methods for ARDS-related mortality remain elusive. Accurate risk assessment is crucial for timely intervention and improved patient outcomes. Machine learning (ML) techniques have emerged as promising tools for mortality prediction in patients with ARDS, leveraging complex clinical datasets to identify key prognostic factors. However, the efficacy of ML-based models remains uncertain. This systematic review aims to assess the value of ML models in the early prediction of ARDS mortality risk and to provide evidence supporting the development of simplified, clinically applicable ML-based scoring tools for prognosis.

Authors

  • Ruimin Tan
    School of Clinical Medical, North China University of Science and Technology, Tangshan, Hebei, China.
  • Chen Ge
    Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China. gechen@iphy.ac.cn.
  • Zhe Li
  • Yating Yan
    School of Clinical Medical, North China University of Science and Technology, Tangshan, Hebei, China.
  • He Guo
    School of Software Technology, Dalian University of Technology, Dalian, 116620, China.
  • Wenjing Song
    Central Theater Center for Disease Control and Prevention of PLA, Beijing, China.
  • Qiong Zhu
    Department of Orthopaedics, The People's Hospital of Shizhu, Chongqing, China.
  • Quansheng Du

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