Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study.

ABSTRACT: Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration-time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. Pediatric autoimmune patients' data were collected at Nanfang Hospital between June 2018 and June 2023. Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Ar

SUBMITTER: Zheng P 

PROVIDER: S-EPMC11672284 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature