Machine learning prediction model for treatment responders in patients with primary biliary cholangitis.

ABSTRACT:

Background and aim

Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data.

Methods

We conducted a single-center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out

SUBMITTER: Kimura N 

PROVIDER: S-EPMC10290270 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature