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Updated: May 20, 2025
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
Published on: September 6, 2024
An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical
Pu Wang1, Teng-Hui Chen2, Mei-Yun Chang2
1Department of Biomedical Engineering, Fourth Military Medical University, Xi'an 710032, China; Tianjin International Joint Research Centre for Neural Engineering, and Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Background:
Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and diaphragmatic injury, and may lead to worsening clinical weaning outcomes. The present study proposes a comprehensive machine learning (ML) framework for predicting the weaning outcomes of patients with PMV, without relying on ventilator data, by utilizing features from electrical impedance tomography (EIT).
Methods:
EIT data from 58 patients with PMV were analyzed. Extracted EIT image features were standardized using the min-max method. The Boruta method was employed to select significant features for the ML model. To balance the data, the SMOTE method was utilized. Ten ML algorithms commonly used in clinical prediction were compared. The SHAP and LIME methods were used to explain the ML models. Feature selection, data balancing, and hyperparameter adjustment all adopt the Leave-One-Out cross-validation method to avoid overfitting.
Results:
The area under the receiver operating characteristic (AUC), specificity, and precision of the ML model with SMOTE balance were significantly improved (p < 0.05) compared to unbalanced data. However, the sensitivity was reduced considerably (p = 0.02). The optimal ML model, extreme gradient boost (XGBoost), demonstrated excellent performance: AUC = 0.862, sensitivity = 0.923, specificity = 0.800, accuracy = 0.889, precision = 0.923, and f-score = 0.923. Decision Curve Analysis and calibration curve evaluation indicated that the model has high clinical generality and reliability. The SHAP and LIME methods enabled model interpretation at both the global and individual sample levels.
Conclusion:
The weaning outcome prediction model based on EIT data does not rely on ventilator data, which is suitable for a broader range of weaning scenarios. We proposed a comprehensive ML framework for weaning outcome prediction and incorporated the SHAP and LIME methods, which significantly improved the interpretability of the model.
