Toward clinical implementation of interpretable machine learning for early risk stratification in acute pancreatitis
Abstract
We read with great interest the article by Cisnal et al. entitled “Improved early prediction of acute pancreatitis severity using SHAP-based XGBoost model: Beyond traditional scoring systems.” [1] The authors should be commended for leveraging a contemporary machine-learning framework and interpretability methods (SHAP) to address an enduring clinical challenge: identifying, as early as possible, patients who will progress to moderately severe or severe acute pancreatitis (MSAP/SAP). We believe the work is promising and could be further strengthened by clarifying several practical aspects that directly affect clinical implementation and generalizability.
Fields of Study & Subjects
Pancreatitis Pathology and Treatment
Artificial Intelligence in Healthcare
Sepsis Diagnosis and Treatment
Publication details
- Journal / source
- Digestive and Liver Disease
- Volume
- 58
- Issue
- 4
- Pages
- 577–578
- Publication date
- 12 January 2026
- Language
- EN