Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study.

ABSTRACT:

Background

Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations.

Objective

To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables.

Methods

5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness,

SUBMITTER: Yuan L 

PROVIDER: S-EPMC9707001 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature