Publications by authors named "Zong-Xing Lu"
Article Synopsis
- This study developed a machine learning ensemble model for monitoring valproic acid (VPA) in pediatric epilepsy patients to improve clinical accuracy in treatment.
- The model utilized data from 252 patients, using various algorithms like Gradient Boosting Regression Trees and Random Forest Regression, achieving high relative accuracy (87.8%) and low error rates.
- Key factors affecting VPA levels included platelet count and daily dose, indicating the model's potential to enhance clinical decision-making in VPA management.
Aims: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients.
Methods: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set.