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Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques

Negin Ashrafi, Armin Abdollahi, Maryam Pishgar

TL;DR

This paper tackles predicting ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients using advanced machine learning applied to the MIMIC-III dataset. It introduces a refined feature selection approach via CatBoost, balanced training with SMOTE, and cross-validated hyperparameter tuning across six models, with XGBoost achieving the best performance (AUC 0.940, accuracy 0.875) and markedly surpassing prior work. SHAP analysis identifies ICU/hospital length of stay and specific laboratory and clinical indicators as key predictors, while an ablation study confirms that all 15 selected features meaningfully contribute to predictive accuracy. The work demonstrates improved early detection potential and provides a foundation for real-time clinical decision support, while acknowledging limitations such as dataset age and lack of temporal modeling, and suggesting avenues for external validation and broader data integration.

Abstract

Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patients and healthcare systems. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. Methods: We implemented six machine learning models using the MIMIC-III database. Our methodology included preprocessing steps, such as feature selection with CatBoost and expert opinion, addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning through 5-fold cross-validation to optimize hyperparameters. Key models evaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and AdaBoost. Additionally, we conducted SHAP analysis to determine feature importance and performed an ablation study to assess feature impacts on model performance. Results: XGBoost outperformed the baseline models and the best existing literature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity, F1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an AUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than the best results in the existing literature, with an AUC of 0.706 and an Accuracy of 0.640, respectively. This enhanced performance underscores the models' effectiveness in clinical settings. Conclusions: This study enhances the predictive modeling of VAP in TBI patients, improving early detection and intervention potential. Refined feature selection and advanced ensemble techniques significantly boosted model accuracy and reliability, offering promising directions for future clinical applications and medical diagnostics research.

Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques

TL;DR

This paper tackles predicting ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients using advanced machine learning applied to the MIMIC-III dataset. It introduces a refined feature selection approach via CatBoost, balanced training with SMOTE, and cross-validated hyperparameter tuning across six models, with XGBoost achieving the best performance (AUC 0.940, accuracy 0.875) and markedly surpassing prior work. SHAP analysis identifies ICU/hospital length of stay and specific laboratory and clinical indicators as key predictors, while an ablation study confirms that all 15 selected features meaningfully contribute to predictive accuracy. The work demonstrates improved early detection potential and provides a foundation for real-time clinical decision support, while acknowledging limitations such as dataset age and lack of temporal modeling, and suggesting avenues for external validation and broader data integration.

Abstract

Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patients and healthcare systems. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. Methods: We implemented six machine learning models using the MIMIC-III database. Our methodology included preprocessing steps, such as feature selection with CatBoost and expert opinion, addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning through 5-fold cross-validation to optimize hyperparameters. Key models evaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and AdaBoost. Additionally, we conducted SHAP analysis to determine feature importance and performed an ablation study to assess feature impacts on model performance. Results: XGBoost outperformed the baseline models and the best existing literature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity, F1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an AUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than the best results in the existing literature, with an AUC of 0.706 and an Accuracy of 0.640, respectively. This enhanced performance underscores the models' effectiveness in clinical settings. Conclusions: This study enhances the predictive modeling of VAP in TBI patients, improving early detection and intervention potential. Refined feature selection and advanced ensemble techniques significantly boosted model accuracy and reliability, offering promising directions for future clinical applications and medical diagnostics research.
Paper Structure (18 sections, 7 figures, 5 tables, 1 algorithm)

This paper contains 18 sections, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Background
  2. Methodologies
  3. Data Source and Inclusion Criteria
  4. Patient Extraction
  5. Feature Selection
  6. Data Preprocessing
  7. Ablation process
  8. Modeling
  9. RESULTS
  10. Statistical Analysis between Cohorts
  11. Ablation Study
  12. Evaluation results
  13. SHAP analysis
  14. DISCUSSION
  15. Summary of Existing Model Compilation
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Flow diagram of the patient selection process
  • Figure 2: Top 15 features based on CatBoost feature importance scores, highlighting the most impactful ones.
  • Figure 3: Data preprocessing workflow, illustrating the steps from patient selection to the creation of the final dataset.
  • Figure 4: Ablation study for proposed XGBoost model
  • Figure 5: ROC curves of the eight models for the training set. ROC curves of the six models for the test set. SVM, Logistic Regression, XGB, RF, ANN, AdaBoost.
  • ...and 2 more figures