⚡ Quick Summary
This study developed an explainable machine learning model to predict 28-day all-cause mortality in immunocompromised patients in the ICU, utilizing data from the MIMIC-IV database. The Support Vector Machine (SVM) model achieved the highest performance with an AUROC of 0.863, highlighting its potential for clinical decision-making.
🔍 Key Details
- 📊 Dataset: MIMIC-IV database, ICU admissions from 2008 to 2019
- 🧩 Patient Population: Immunocompromised patients, including those with hematological malignancies and organ transplants
- ⚙️ Technology: Ten machine learning models evaluated, including SVM, XGBoost, and Random Forest
- 🏆 Performance: SVM: AUROC 0.863, AUPRC 0.678
🔑 Key Takeaways
- 📈 Predictive Modeling: The study emphasizes the importance of accurate mortality prediction in vulnerable ICU patients.
- 💡 Key Predictors: Factors such as urine output, BUN levels, and presence of metastatic tumors were significant.
- 🤖 Explainability: SHAP analyses provided insights into how predictive features influenced outcomes.
- 🏥 Clinical Utility: The model’s explainability enhances its applicability in clinical settings.
- 🌍 Research Significance: Findings could improve resource allocation and patient management in ICUs.
📚 Background
Predicting mortality in critically ill patients, especially those who are immunocompromised, is a complex yet vital task in intensive care medicine. Accurate predictions can guide clinical decisions and optimize the use of limited healthcare resources. With advancements in machine learning, there is a growing opportunity to enhance predictive accuracy while maintaining model interpretability.
🗒️ Study
This retrospective cohort study utilized data from the MIMIC-IV database, focusing on immunocompromised patients admitted to the ICU. The researchers aimed to develop a machine learning model that not only predicts 28-day all-cause mortality but also provides insights into the factors influencing these predictions. The study involved a comprehensive evaluation of ten different machine learning algorithms.
📈 Results
Among the evaluated models, the Support Vector Machine (SVM) emerged as the most effective, achieving an AUROC of 0.863 and an AUPRC of 0.678. Key predictive factors identified included 24-hour urine output, blood urea nitrogen (BUN) levels, and the presence of metastatic solid tumors. These findings underscore the importance of specific clinical parameters in mortality risk assessment.
🌍 Impact and Implications
The implications of this study are significant for clinical practice. By utilizing machine learning models that are both accurate and interpretable, healthcare providers can make informed decisions regarding the management of immunocompromised patients in the ICU. This approach not only enhances patient care but also optimizes the allocation of critical resources in healthcare settings.
🔮 Conclusion
This study highlights the potential of explainable machine learning in predicting mortality among immunocompromised ICU patients. The identified predictive factors can serve as crucial indicators in clinical practice, paving the way for improved patient outcomes and resource management. Continued research in this area is essential to further refine these models and enhance their applicability in diverse clinical scenarios.
💬 Your comments
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Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.
Abstract
OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality prediction is crucial for clinical decision-making and optimal allocation of critical care resources for this vulnerable patient population.
METHODS: We utilized retrospective clinical data from the MIMIC-IV (version 2.2) database, encompassing ICU admissions at Beth Israel Deaconess Medical Center from 2008 to 2019. Eligible immunocompromised patients, including those with primary immunodeficiencies and chronic acquired conditions, such as hematological malignancies, solid tumors, and organ transplantation, were selected. Data were randomly split into training (80%) and testing (20%) cohorts. Ten ML models (logistic regression, XGBoost, LightGBM, AdaBoost, Random Forest, Gradient Boosting, Gaussian Naive Bayes, Complement Naive Bayes, Multilayer Perceptron, and Support Vector Machine) were developed and evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, accuracy, and F1 score. Model explainability was achieved through SHapley Additive exPlanations (SHAP), and decision curve analysis (DCA) assessed clinical utility. In addition, Cox proportional hazards regression was conducted to evaluate the impact of predictive factors on time-to-event outcomes.
RESULTS: Among the evaluated models, the Support Vector Machine (SVM) demonstrated the highest AUROC of 0.863 (95% CI 0.834-0.890) and a notable AUPRC of 0.678 (95% CI 0.624-0.736). Key predictive factors consistently identified across multiple ML models included 24-h urine output, blood urea nitrogen (BUN) levels, presence of metastatic solid tumors, Charlson Comorbidity Index (CCI), and international normalized ratio (INR). SHAP analyses provided detailed insights into how these features influenced model predictions.
CONCLUSIONS: The explainable ML models based on various artificial intelligence methods demonstrated promising clinical applicability in predicting 28-day mortality risk among immunocompromised ICU patients. Factors such as urine output, BUN, metastatic solid tumors, CCI, and INR significantly contributed to prediction outcomes and may serve as important predictors in clinical practice.
Author: [‘Yu Z’, ‘Fang L’, ‘Ding Y’]
Journal: Eur J Med Res
Citation: Yu Z, et al. Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database. Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database. 2025; 30:358. doi: 10.1186/s40001-025-02622-3