Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
PMID: 40359510

Abstract

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy.

Authors

  • Jinxi Yang
    The Second Clinical Medical College, Harbin Medical University, Heilongjiang Province, Harbin, China.
  • Siyao Zeng
    The Second Clinical Medical College, Harbin Medical University, Heilongjiang Province, Harbin, China.
  • Shanpeng Cui
    The Second Clinical Medical College, Harbin Medical University, Heilongjiang Province, Harbin, China.
  • Junbo Zheng
    Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, Harbin, China.
  • Hongliang Wang
    Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.

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