Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics
This work addresses the need for interpretable modeling of composite treatments in biomedical applications, specifically for understanding nutrient impacts on blood glucose, but it is incremental as it builds on existing nonparametric methods.
The authors tackled the problem of estimating the separate and joint effects of multiple nutrients on blood glucose dynamics by developing a new convolution-based model and extending probabilistic nonparametric approaches, resulting in improved prediction accuracy over existing methods.
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.