相关共变体的机器学习辅助选:应用于desipramine临床数据的应用
Innocent Gerald Asiimwe1,2, Bonginkosi S'fiso Ndzamba3,4, Samer Mouksassi5
1The Wolfson Centre for Personalized Medicine, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. i.asiimwe@liverpool.ac.uk.
机器学习 (ML) 通过预先选来改善药理学中的共变量选择,大大降低了计算负担. 计算高度相关的共变量可以提高ML的准确性,从而使模型开发更有效,更可靠.
科学领域:
- 药理计量学和计算生物学
- 统计建模和机器学习
背景情况:
- 药量计学中的逐步共变量建模 (SCM) 是计算密集型的,容易选择不正确的共变量.
- 机器学习 (ML) 为高效的共变量查提供了潜在的解决方案,但其在临床数据中的应用需要进一步研究.
研究的目的:
- 为了比较各种ML和传统药量测量 (PMX) 技术对共变量选择的性能.
- 评估高度相关的共变量对ML和SCM性能的影响.
- 确定临床数据中预先选的最佳ML算法和共变量数.
主要方法:
- 基于15个共变量 (4有影响力) 的desipramine人口-药动力学模型的模拟数据集.
- 比较了ML算法 (例如,回归) 和SCM,并没有考虑高度相关的共变量.
- 使用F1分数和计算运行时间评估性能.
主要成果:
- 回归显示了ML技术中最高的精度 (F1得分 = 0.475 ± 0.231),在考虑相关性时显著改善到0.860 ± 0.158.
- ML显著优于SCM,特别是当考虑相关性时 (ML:0.860与SCM:0.499).
- 斜坡回归比SCM快约2万倍,减少了临床SCM运行时间42.86%,并产生了相同的最终模型.
结论:
- 考虑到高度相关的共变量,可以大大提高ML在药量模型中的预选准确性.
- ML,特别是回归,是用于共变量预选的计算效率高,准确的工具,改进了传统的SCM.
- 模拟研究对于指导ML方法的选择和确定预选的最佳共变量数量至关重要.
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