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.

在生物医学应用中，我们通过使用多个组成部分的组合治疗，评估生理反应，并学习组成部分的单独效果和联合效果。我们通过扩展现有的概率非参数方法来解决该问题，并开发出一种新的基于卷积的组合治疗 - 反应曲线模型，具有更好的生物学解释性。通过调整治疗组成部分，整合剂量，并通过层次多输出高斯过程在患者之间共享统计信息，我们的方法提高了对血糖预测的准确性，并能解释碳水化合物和脂肪对总体血糖反应的不同效果。

多种营养素对血糖动力学的综合效应的非参数建模

Nonparametric modeling of the composite effect of multiple nutrients on  blood glucose dynamics

Pain is a subjective experience commonly measured through patient's self
report. While there exist numerous situations in which automatic pain
estimation methods may be preferred, inter-subject variability in physiological
and behavioral pain responses has hindered the development of such methods. In
this work, we address this problem by introducing a novel personalized
multitask machine learning method for pain estimation based on individual
physiological and behavioral pain response profiles, and show its advantages in
a dataset containing multimodal responses to nociceptive heat pain.

本研究提出了一种新的个性化多任务机器学习方法，利用个体生理和行为对痛苦的反应来估计疼痛水平，并在有多种反应的数据集中，表明其优势。