A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters

Abstract The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therap...

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Détails bibliographiques
Auteurs principaux: Xiuqing Zhu, Wencan Huang, Haoyang Lu, Zhanzhang Wang, Xiaojia Ni, Jinqing Hu, Shuhua Deng, Yaqian Tan, Lu Li, Ming Zhang, Chang Qiu, Yayan Luo, Hongzhen Chen, Shanqing Huang, Tao Xiao, Dewei Shang, Yuguan Wen
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Medicine
R
Science
Q
Accès en ligne:https://doaj.org/article/36e436b9525e4b3b9f9f54c1a7914a6c
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https://doaj.org/article/36e436b9525e4b3b9f9f54c1a7914a6c

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