RT info:eu-repo/semantics/article
T1 Improved early prediction of acute pancreatitis severity using SHAP-based XGBoost model: Beyond traditional scoring systems
A1 Cisnal De La Rica, Ana
A1 Ruiz Rebollo, María Lourdes
A1 Flórez Pardo, César
A1 Matesanz Isabel, Jessica
A1 Pérez Turiel, Javier
A1 Fraile Marinero, Juan Carlos
K1 Acute pancreatitis
K1 Clinical decision support
K1 Machine learning
K1 Severity prediction
AB Background: Acute pancreatitis (AP) progresses to severe forms in about 20 % of cases, leading to highmorbidity 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 severeand 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 developedusing admission (24-hour) and early (48-hour) data. Models were trained and tested using an 80:20 strat-ified split and evaluated based on ROC-AUC. F-Anova, Mutual Information and SHapley Additive exPlana-tions (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 predic-tive performance with ROC-AUCs of 0.89 (24-hour) and 0.94 (48-hour) on the test cohort. Key predictivefeatures included SIRS, BUN, CRP, creatinine, and pleural effusion. Compared to Ranson and BISAP (bothROC-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 earlyprediction of AP severity, improving clinical decision-making and patient management.
PB Elsevier
SN 1590-8658
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/80441
UL https://uvadoc.uva.es/handle/10324/80441
LA eng
NO Digestive and Liver Disease, 2025.
NO Producción Científica
DS UVaDOC
RD 12-ene-2026