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An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers
Jun Zhou1,2, Ying Chen1,2, Jingping Liu1,2
1Department of Laboratory Medicine, the First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China.
BMC Medical Research Methodology
|June 25, 2026
Summary
This study developed a machine learning model to predict 30-day mortality in acute pancreatitis (AP) patients using five key lab parameters. The XGboost algorithm demonstrated strong predictive performance, offering a potential tool for risk stratification.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Clinical Prediction Models
Background:
- Acute pancreatitis (AP) poses a significant mortality risk.
- Accurate prediction of 30-day mortality in AP is crucial for timely intervention.
- Existing prognostic scores may have limitations in predicting AP outcomes.
Purpose of the Study:
- To develop and validate a machine learning (ML) algorithm for predicting 30-day mortality in adult AP patients.
- To identify key laboratory parameters for accurate mortality prediction.
- To create an interpretable ML model for clinical utility.
Main Methods:
- Retrospective analysis of 965 AP patients.
- Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
- Evaluation of eleven ML algorithms, with performance assessed by AUC.
- Model interpretation using SHapley additive explanation (SHAP).
Main Results:
- Extreme gradient boosting (XGboost) showed the best performance.
- Key predictors identified: creatine kinase (CK), lactate dehydrogenase (LDH), age, prothrombin time (PT), and carbohydrate antigen 19-9 (CA19-9).
- A five-feature XGboost model achieved high AUC (1.000 development, 0.847 validation).
Conclusions:
- An interpretable XGboost algorithm using five parameters can predict 30-day mortality in AP.
- The model shows promising internal performance but requires external validation.
- A research prototype is available, but not for clinical decision-making.
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