人口
统计
药代动力学
采样(信号处理)
均方误差
贝叶斯概率
数学
最大后验估计
人工智能
计算机科学
医学
药理学
最大似然
环境卫生
滤波器(信号处理)
计算机视觉
作者
Alexandre Destère,Pierre Marquet,Charlotte Salmon Gandonnière,Anders Åsberg,Véronique Loustaud‐Ratti,Paul Carrier,Stéphan Ehrmann,Chantal Barin‐Le Guellec,Aurélie Prémaud,Jean‐Baptiste Woillard
标识
DOI:10.1007/s40262-022-01138-x
摘要
Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation. The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients. The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset). In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.
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