Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.

ABSTRACT:

Background

Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model to calculate the risk of mortality in patients with AP admitted in intensive care unit (ICU) and compared it with existing scoring systems.

Methods

A gradient-boosting ML (XGBoost) model was developed and externally validated based on two public databases: Medical Information Mart for Intensive Care (MIMIC, training cohort) and the eICU Collaborative Research Database (eICU-CRD, validation cohort). We compared the performance of the XGBoost model with validated clinical risk scoring systems (the APACHE IV, SOFA, and Bedside Index

SUBMITTER: Jiang M 

PROVIDER: S-EPMC11877909 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature