pyDarwin 机器学习算法在非线性混合效应模型选择和优化中的应用和比较。

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY
Xinnong Li, Mark Sale, Keith Nieforth, James Craig, Fenggong Wang, David Solit, Kairui Feng, Meng Hu, Robert Bies, Liang Zhao
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引用次数: 0

摘要

自 NONMEM® 推出以来,前向加法/后向除法(FABE)一直是群体药代动力学模型选择(PPK)的标准。我们研究了五种机器学习(ML)算法(遗传算法[GA]、高斯过程[GP]、随机森林[RF]、梯度提升随机树[GBRT]和粒子群优化[PSO])作为 FABE 的替代方法。这些算法被应用于 PPK 模型选择,重点是比较它们各自的效率和鲁棒性。所有机器学习算法都包括 ML 算法与局部下坡搜索的结合。局部下坡搜索包括每次系统地改变一个或两个 "特征"(一位或两位局部搜索),与 ML 方法交替进行。穷举搜索(所有可能的模型特征组合,N = 1,572,864 个模型)是衡量鲁棒性的黄金标准,而在确定最终模型之前所检查的模型数量则是衡量效率的指标。GA、RF、GBRT 和 GP 只用一位局部搜索就能确定最佳模型。PSO 则需要两位局部下坡搜索。在我们的分析中,从找到最优解之前所检查的模型数量(495 个模型)来看,GP 是效率最高的算法,而 PSO 的效率最低,在找到最优解之前需要 1710 个独特的模型。此外,GP 也是耗时最长的算法,需要 2975.6 分钟,而 GA 只需要 321.8 分钟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.

pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.

Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two "features" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.

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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
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