Machine learning-based prediction of response to Ustekinumab with Crohn's disease.

ABSTRACT:

Background

A reliable approach to predict the response to Ustekinumab (UST) in patients with Crohn's disease (CD) is lacking.

Objectives

This study aims to develop and validate machine learning (ML) models to predict the response to UST and further achieve personalized therapy.

Design

Retrospective multi-center study.

Methods

This study included 162 CD patients treated with UST between May 2022 and May 2024. Four ML algorithms (extreme gradient boosting, random forest, logistic regression, and support vector machine) were integrated to identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was used for visual explainability. Two models were established to forecast the response to UST, with the outcomes of the response situation at w

SUBMITTER: Xiong Z 

PROVIDER: S-EPMC12477378 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature