Machine learning for temporary stoma after intestinal resection in surgical decision-making of Crohn's disease.

ABSTRACT:

Background

Crohn's disease (CD) often necessitates surgical intervention, with temporary stoma creation after intestinal resection (IR) being a crucial decision. This study aimed to construct novel models based on machine learning (ML) to predict temporary stoma formation after IR for CD.

Methods

Patient data who underwent IR for CD at our center between July 2017 and March 2023 were collected for inclusion in this retrospective study. Eligible CD patients were randomly divided into training and validation cohorts. Feature selection was executed using the least absolute shrinkage and selection operator. We employed three ML algorithms including traditional logistic regression, novel random forest and XG-Boost to create prediction models. The area under the curve (AUC), accur

SUBMITTER: Wang FT 

PROVIDER: S-EPMC11863836 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature