Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data.

ABSTRACT:

Objective

To construct a prediction model for teicoplanin (TEIC) plasma concentrations through machine learning and deep learning techniques in patients with liver disease using real-world clinical data.

Methods

A retrospective study was conducted on patients who underwent TEIC therapeutic drug monitoring at a tertiary hospital in China (January 2019-March 2025). Dataset was split into training and test sets (8:2 ratio). Feature selection combined univariate analysis and algorithm importance ranking. Missing values were imputed using random forest (RF) model. Ten machine learning algorithms, such as RF, TransTab and light gradient boosting machine (LightGBM), were employed for model development, with predictive performance evaluated through 10-fold cross-validation on the tr

SUBMITTER: Jian F 

PROVIDER: S-EPMC12715011 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature