对人口药理动学的共变模型选择方法:对现有方法的系统审查,从SCM到AI
Mélanie Karlsen1,2, Sonia Khier3,4, David Fabre2
1LIRMM, Laboratory of Computer Science, Robotics and Microelectronics in Montpellier, CNRS, Montpellier University, Montpellier, France.
这次系统性审查发现,基于EBE的ML方法在人群PK (popPK) 建模中对共变量选择优越. 混合基因算法AALASSO成为顶级的共变模型选择技术.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
- 统计建模 统计建模
背景情况:
- 种群药动力学 (popPK) 建模越来越多地使用共变型建模方法.
- 缺乏比较研究阻碍了对现有的popPK共变量建模技术的理解.
研究的目的:
- 系统地审查和评估popPK共变量建模方法的性能.
- 确定最有效的共同变量选择和建模技术.
主要方法:
- 对popPK共变量建模方法的系统文献综述.
- 报告评估设置,性能指标和计算时间.
- 对方法性能现有知识的分析.
主要成果:
- 基于经验贝叶斯估计 (EBE) 的机器学习 (ML) 方法被确定为顶级共变量选择方法.
- AALASSO (混合遗传算法),FREM (具有临床意义) 和SCM+ (具有阶段过) 是最佳的共变模型选择技术,其中AALASSO优越.
- 在对比模拟数据集和绩效评估指标方面缺乏共识.
结论:
- AALASSO是最有效的共同变量模型选择技术.
- 为了进行可靠的方法评估,需要对绩效指标 (TPR, FPR, FNR, TNR, MPE, RMSE, BIC) 的标准化报告和开源基准数据集.
- 建议将共变量选择技术与SCM或FFEM等既定方法相结合,以便在未来进行比较.
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