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

arXiv: https://arxiv.org/abs/2311.03129 · HTML: https://mlsanity.com/paper/2311.03129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [A Framework for Interdomain and Multioutput Gaussian Processes](https://mlsanity.com/paper/2003.01115/) (2020)
- [Probabilistic Machine Learning for Healthcare](https://mlsanity.com/paper/2009.11087/) (2020)
- [Observational-Interventional Priors for Dose-Response Learning](https://mlsanity.com/paper/1605.01573/) (2016)
- [Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes](https://mlsanity.com/paper/1906.00226/) (2019)
- [Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization](https://mlsanity.com/paper/2109.09261/) (2021)
- [Errors-in-variables Modeling of Personalized Treatment-Response Trajectories](https://mlsanity.com/paper/1906.03989/) (2019)

## Abstract (arXiv)

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.