在药物动力学建模中,应用SHAP值来推断共变量的最佳功能形式
Alexander Janssen1, Mark Hoogendoorn2, Marjon H Cnossen3
1Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, Amsterdam, The Netherlands.
CPT: pharmacometrics & systems pharmacology
|December 15, 2023
概括
本研究介绍了SHapley添加剂扩展 (SHAP) 方法,以改善群体药理动力学 (PK) 模型中的共同变量选择. SHAP增强了机器学习方法,为识别有影响力的共变量提供了传统方法的强大替代方案.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 机器学习应用 机器学习应用
- 生物统计学 生物统计学
背景情况:
- 种群药动力学 (PK) 模型使用共变量解释了个体间的变异性.
- 传统的共同变量选择方法,如前进的逐步选择,容易产生偏差和不正确的共同变量包含.
- 在PK模型中实现共变量的最佳功能形式通常是未知的,导致潜在的不准确性.
研究的目的:
- 研究使用夏普利添加式解释 (SHAP) 来从机器学习 (ML) 模型中推断与PK参数的共变量关系.
- 评估SHAP作为一种在PK建模中为共变量选择适当的功能形式的方法.
- 为传统的共变量分析方法提供替代方案.
主要方法:
- 使用了SHapley添加式扩展 (SHAP) 来解释ML模型 (随机森林,XGBoost).
- 将该方法应用于一个涉及119名血友病A患者的病例研究,这些患者接受了凝血因子VIII度.
- 预测的经验贝叶斯估计清除和中央体积从一个基础非线性混合效应模型.
主要成果:
- SHAP成功地确定了共变量关系,与PK分析中以前的发现相一致.
- 该方法揭示了共变量之间的微妙,复杂的相互作用,这些相互作用很难用传统分析来检测.
- SHAP展示了它在ML模型中推断最佳功能形式以实现共变量的能力.
结论:
- 在人群PK建模中,SHAP可以扩展基于ML的共同变量选择.
- 拟议的方法为经典的共变量分析提供了潜在的替代方案,减少了偏差并提高了准确性.
- SHAP对共变量分析具有完整的完整模型方法的承诺.
相关概念视频
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