Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · 2025
Improved early prediction of acute pancreatitis severity using SHAP-based XGBoost model: Beyond traditional scoring systems.
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Cisnal Ana, Ruiz Rebollo María Lourdes, Flórez-Pardo César, Matesanz-Isabel Jessica, Pérez Turiel Javier, Fraile Juan Carlos
Аннотация
BACKGROUND: Acute pancreatitis (AP) progresses to severe forms in about 20 % of cases, leading to high morbidity and mortality. Traditional clinical scoring systems for severity prediction (e.g., Ranson, BISAP), are limited by delayed applicability, and suboptimal diagnostic accuracy.
AIMS: To develop and validate machine learning (ML) models for early prediction of moderately severe and severe acute pancreatitis (MSAP-SAP), and to compare them with conventional scores.
METHODS: A retrospective cohort of 816 patients (2014-2023) was analyzed. ML models were developed using admission (24-hour) and early (48-hour) data. Models were trained and tested using an 80:20 stratified split and evaluated based on ROC-AUC. F-Anova, Mutual Information and SHapley Additive exPlanations (SHAP) were used for feature selection. SHAP was also used for model interpretability.
RESULTS: The XGBoost model with SHAP-based feature selection (XGBSH) achieved the highest predictive performance with ROC-AUCs of 0.89 (24-hour) and 0.94 (48-hour) on the test cohort. Key predictive features included SIRS, BUN, CRP, creatinine, and pleural effusion. Compared to Ranson and BISAP (both ROC-AUC = 0.72), the XGBSH models demonstrated superior accuracy and allowed flexible, threshold-based classification.
CONCLUSION: The proposed SHAP-enhanced XGBoost model offers a reliable and interpretable tool for early prediction of AP severity, improving clinical decision-making and patient management.
AIMS: To develop and validate machine learning (ML) models for early prediction of moderately severe and severe acute pancreatitis (MSAP-SAP), and to compare them with conventional scores.
METHODS: A retrospective cohort of 816 patients (2014-2023) was analyzed. ML models were developed using admission (24-hour) and early (48-hour) data. Models were trained and tested using an 80:20 stratified split and evaluated based on ROC-AUC. F-Anova, Mutual Information and SHapley Additive exPlanations (SHAP) were used for feature selection. SHAP was also used for model interpretability.
RESULTS: The XGBoost model with SHAP-based feature selection (XGBSH) achieved the highest predictive performance with ROC-AUCs of 0.89 (24-hour) and 0.94 (48-hour) on the test cohort. Key predictive features included SIRS, BUN, CRP, creatinine, and pleural effusion. Compared to Ranson and BISAP (both ROC-AUC = 0.72), the XGBSH models demonstrated superior accuracy and allowed flexible, threshold-based classification.
CONCLUSION: The proposed SHAP-enhanced XGBoost model offers a reliable and interpretable tool for early prediction of AP severity, improving clinical decision-making and patient management.