In biomedical applications it is often necessary to estimate a physiological response to a treatment consisting of multiple components, and learn the separate effects of the components in addition to the joint effect. Here, we extend existing probabilistic nonparametric approaches to explicitly address this problem. We also develop a new convolution-based model for composite treatment-response curves that is more biologically interpretable. We validate our models by estimating the impact of carbohydrate and fat in meals on blood glucose. By differentiating treatment components, incorporating their dosages, and sharing statistical information across patients via a hierarchical multi-output Gaussian process, our method improves prediction accuracy over existing approaches, and allows us to interpret the different effects of carbohydrates and fat on the overall glucose response.

翻译：在生物医学应用中，通常需要估计由多种成分组成的治疗方案所产生的生理反应，并学习各成分的单独效应及联合效应。本文对现有概率非参数方法进行扩展，以显式解决这一问题。同时，我们提出了一种新的基于卷积的复合治疗-反应曲线模型，该模型具有更强的生物学可解释性。通过估计膳食中碳水化合物和脂肪对血糖的影响，我们验证了所提出的模型。通过区分治疗成分、纳入其剂量信息以及通过分层多输出高斯过程共享患者间的统计信息，我们的方法在预测准确性上优于现有方法，并能够解释碳水化合物和脂肪对整体血糖反应的不同效应。