深度隔间模型:一种深度学习方法,用于可靠地预测药理动力学建模中的时间序列数据
Alexander Janssen1, Frank W G Leebeek2, 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
深模型 (DCM) 准确地预测药物度,即使数据有限,也优于传统的非线性混合效应 (NLME) 模型. 这种先进技术有助于模拟治疗策略和治疗药物监测.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 机器学习在药物开发中的作用
- 计算生物学 计算生物学
背景情况:
- 非线性混合效应 (NLME) 模型是分析患者药物反应的标准,但复杂且耗时.
- 现有的机器学习方法经常在治疗策略的训练数据之外的可靠推断方面扎.
研究的目的:
- 引入深层隔间模型 (DCM),集成神经网络和普通微分方程.
- 为了证明DCM在小数据集的准确性及其在预测药物度方面优于NLME模型的优势.
- 验证DCM能够描述药物度随时间变化的能力,并使治疗策略模拟成为可能.
主要方法:
- 开发了一种新的深模型 (DCM),结合了神经网络和普通微分方程.
- 使用不同大小的模拟数据集验证了DCM.
- 将DCM应用于在手术期间接受因子VIII缩剂的血友病A患者的真实数据集.
主要成果:
- 即使在小型数据集上进行训练时,DCM也表现出了准确性.
- 与以前的NLME模型相比,DCM提供了先验药物度的更准确的预测.
- DCM有效地描述了药物度随时间的动态变化.
结论:
- 深模型 (DCM) 为传统的NLME模型提供了强大而准确的替代方案.
- 该DCM的药理动力学原理有助于模拟各种治疗策略.
- 该DCM可实现先进的治疗药物监测,以改善患者护理.
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