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March 16, 2026BMC Medical Informatics and Decision Making0 citationsOpen Access

Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients — a study based on the MIMIC-IV database

RMRiliang MaThe People's Hospital of Guangxi Zhuang Autonomous RegionHWHong WangOak Ridge National LaboratoryCLChengmei LvGuangxi Medical University

Key Points

  • The study aims to enhance the predictability of postoperative respiratory failure using machine learning techniques.
  • Utilized the MIMIC-IV database for data analysis
  • Implemented a Gradient Boosting Machine (GBM) model for prediction
  • Conducted SHAP analysis to interpret model outputs
  • The GBM model achieved balanced performance in discrimination and calibration.
  • SHAP analysis revealed critical hemodynamic and metabolic markers affecting PRF predictions.

Abstract

The GBM model, selected for its balanced performance across discrimination, calibration, and validation stability, provides a promising tool for early PRF risk stratification. The use of SHAP analysis enhances the interpretability of the model, highlighting the role of hemodynamic and metabolic markers in predicting PRF, thus improving clinical understanding and decision-making.

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Cite This Study

Ma et al. (2026) studied this question.

synapsesocial.com/papers/69b79df38166e15b153ab17fhttps://doi.org/10.1186/s12911-026-03425-0
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