Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning.

ABSTRACT:

Objective

To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method.

Methods

A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with h

SUBMITTER: Ma P 

PROVIDER: S-EPMC8963816 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature