论文标题
通过神经 - 药代动力学/药效学建模对患者反应时间课程的深度学习预测
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling
论文作者
论文摘要
目前，使用药代动力学/药物动力学（PK/PD）方法来对患者反应时间课程进行纵向分析，该方法目前需要在动态系统的建模中进行大量的人类经验和专业知识。通过利用深度学习的最新进步，我们表明可以直接从纵向患者数据中学到管理差异方程式。特别是，我们提出了一种新型的神经PK/PD框架，将关键药理原理与神经普通微分方程相结合。我们将其应用于由600多名患者组成的临床数据集的药物浓度和血小板反应分析。我们表明，关于时间预测的指标，神经PK/PD模型在最先进的模型上有所改善。此外，通过将关键的PK/PD概念纳入其体系结构，该模型可以概括并实现患者对未经测试给药方案的反应的模拟。这些结果证明了神经PK/PD对患者反应时间过程的自动预测分析的潜力。
The longitudinal analysis of patient response time course following doses of therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic (PK/PD) methodologies, which requires significant human experience and expertise in the modeling of dynamical systems. By utilizing recent advancements in deep learning, we show that the governing differential equations can be learnt directly from longitudinal patient data. In particular, we propose a novel neural-PK/PD framework that combines key pharmacological principles with neural ordinary differential equations. We applied it to an analysis of drug concentration and platelet response from a clinical dataset consisting of over 600 patients. We show that the neural-PK/PD model improves upon a state-of-the-art model with respect to metrics for temporal prediction. Furthermore, by incorporating key PK/PD concepts into its architecture, the model can generalize and enable the simulations of patient responses to untested dosing regimens. These results demonstrate the potential of neural-PK/PD for automated predictive analytics of patient response time course.
