Exposure prediction and dose optimization of polymyxin B based on bayesian and machine learning

ObjectivesTo explore the application scenarios of maximum a posteriori Bayesian estimation (MAP-BE) and eXtreme Gradient Boosting (XGBoost) in the prediction of polymyxin B (PMB) exposure.MethodsTwo sets of simulations based on the population pharmacokinetic (PopPK) model developed for PMB were used...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Qihan Xu, Xuanyi Li, Shuqi Huang, Qin Ding, Yaqian Li, Nan Yang, Chenhui Deng, Bin Tang, Guoping Yang, Qi Pei
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Frontiers Media S.A. 2026-06-01
Sarja:	Frontiers in Pharmacology
Aiheet:	limited sampling strategy maximum a posteriori Bayesian estimators polymyxin B population pharmacokinetics model XGBoost
Linkit:	https://www.frontiersin.org/articles/10.3389/fphar.2026.1859709/full
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