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Everolimus - Wiley Online Library

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Last Updated: 07 June 2022

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Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles

"The area under the concentration versus time curve is the best indicator of exposure, but measuring it requires taking multiple blood samples. " The aim of this research was to develop machine learning algorithms using pharmacokinetic profiles from kidney transplant recipients, simulation profiles, or both forms, as well as a corresponding maximum A posteriori Bayesian estimates. XGBoost was first tested on 58 patient interdose AUCs estimated using MAP u2010BE, followed by 500 million rich interdose PK profiles simulated using previously published population PK parameters. The contribution of mixing patient and simulation profiles was limited only when they were in balanced numbers, with 500 for each versus patient data alone. ".

Source link: https://onlinelibrary.wiley.com/doi/10.1002/psp4.12810

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions