IEEE transactions on bio-medical engineering · 2026
Explainable Prediction of ICU Transfer in Acute Pancreatitis: A Neural Network Model With SHAP Interpretability for Clinical Decision Support.
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Chen Mingchang, Shi Jiayi
Аннотация
OBJECTIVE: Acute pancreatitis (AP) is a life-threatening disorder commonly observed in emergency departments. Patients with acute pancreatitis may necessitate transfer to the intensive care unit (ICU) if standard treatments prove ineffective. The development of a machine learning (ML) model that can precisely forecast the necessity for ICU admission in patients with acute pancreatitis might dramatically enhance in-hospital healthcare for patients.
METHODS: For this study, data were extracted from the MIMIC-III Clinical Database Care-Vue subset (MIMIC-III v 1.4), Medical Information Mart for Intensive Care-IV (MIMIC-IV v 3.1), and eICU Collaborative Research Database (v 2.0). Fifteen features were identified through Boruta's algorithm, Least Absolute Shrinkage and Selection Operator (Lasso), and Recursive Feature Elimination (RFE) utilizing the XGBoost.
RESULTS: Seven machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network (NNET), etc. were developed based on these features. The neural network approach demonstrated the highest reliability, attaining an AUC of 0.891, sensitivity of 0.852, and specificity of 0.767 in the test set. Meanwhile, an interpretability assessment of the most robust models was performed utilizing SHapley Additive exPlanations (SHAP) to prioritize feature importance and elucidate the final model. This model has been deployed on a web platform: https://icupredict.shinyapps.io/shiny6/.
CONCLUSIONS: The neural network model effectively predicted whether patients with AP would be transferred to the ICU, demonstrating optimal performance.
SIGNIFICANCE: We developed an interpretable ML model for prediction of ICU admission in AP patients. It provides clinicians with a reliable tool for early identification and treatment.
METHODS: For this study, data were extracted from the MIMIC-III Clinical Database Care-Vue subset (MIMIC-III v 1.4), Medical Information Mart for Intensive Care-IV (MIMIC-IV v 3.1), and eICU Collaborative Research Database (v 2.0). Fifteen features were identified through Boruta's algorithm, Least Absolute Shrinkage and Selection Operator (Lasso), and Recursive Feature Elimination (RFE) utilizing the XGBoost.
RESULTS: Seven machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network (NNET), etc. were developed based on these features. The neural network approach demonstrated the highest reliability, attaining an AUC of 0.891, sensitivity of 0.852, and specificity of 0.767 in the test set. Meanwhile, an interpretability assessment of the most robust models was performed utilizing SHapley Additive exPlanations (SHAP) to prioritize feature importance and elucidate the final model. This model has been deployed on a web platform: https://icupredict.shinyapps.io/shiny6/.
CONCLUSIONS: The neural network model effectively predicted whether patients with AP would be transferred to the ICU, demonstrating optimal performance.
SIGNIFICANCE: We developed an interpretable ML model for prediction of ICU admission in AP patients. It provides clinicians with a reliable tool for early identification and treatment.