标题： 药物的患者特异性效应使用具有高斯过程的潜在力模型 显示英文标题

标题： Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes

摘要： 多输出高斯过程（GPs）是一种灵活的贝叶斯非参数框架，在联合建模医疗时间序列数据中患者的生理状态方面已被证明是有用的。 然而，捕捉药物和治疗干预对患者生理状态的短期影响仍然具有挑战性。 我们提出了一种新方法，将干预的影响建模为一个混合高斯过程，该过程由一个捕捉患者生理的GP与一个捕捉治疗对特定生理特征影响的潜在力模型组成。 将多输出GP与包含因果时间标记内核的GP进行卷积，导致了对患者生理状态对干预反应的明确定义的模型。 我们证明了我们的模型导致可解析处理的交叉协方差函数，从而实现了可扩展的推断。 我们的分层模型包括患者特异性效应的估计，但允许在患者之间共享支持。 我们的方法在具有挑战性的医院数据上实现了有竞争力的预测性能，其中我们恢复了患者对三种常见药物的特异性反应：一种降压药和两种抗凝血药。 显示英文摘要

摘要： Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains challenging. We propose a novel approach that models the effect of interventions as a hybrid Gaussian process composed of a GP capturing patient physiology convolved with a latent force model capturing effects of treatments on specific physiological features. This convolution of a multi-output GP with a GP including a causal time-marked kernel leads to a well-characterized model of the patients' physiological state responding to interventions. We show that our model leads to analytically tractable cross-covariance functions, allowing scalable inference. Our hierarchical model includes estimates of patient-specific effects but allows sharing of support across patients. Our approach achieves competitive predictive performance on challenging hospital data, where we recover patient-specific response to the administration of three common drugs: one antihypertensive drug and two anticoagulants.