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

Arina Odnoblyudova, Caglar Hizli, ST John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:428-444, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v225-odnoblyudova23a, title = {Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics}, author = {Odnoblyudova, Arina and Hizli, Caglar and John, ST and Cognolato, Andrea and Juuti, Anne and S\"arkk\"a, Simo and Pietil\"ainen, Kirsi and Marttinen, Pekka}, booktitle = {Proceedings of the 3rd Machine Learning for Health Symposium}, pages = {428--444}, year = {2023}, editor = {Hegselmann, Stefan and Parziale, Antonio and Shanmugam, Divya and Tang, Shengpu and Asiedu, Mercy Nyamewaa and Chang, Serina and Hartvigsen, Tom and Singh, Harvineet}, volume = {225}, series = {Proceedings of Machine Learning Research}, month = {10 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v225/odnoblyudova23a/odnoblyudova23a.pdf}, url = {https://proceedings.mlr.press/v225/odnoblyudova23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics %A Arina Odnoblyudova %A Caglar Hizli %A ST John %A Andrea Cognolato %A Anne Juuti %A Simo Särkkä %A Kirsi Pietiläinen %A Pekka Marttinen %B Proceedings of the 3rd Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2023 %E Stefan Hegselmann %E Antonio Parziale %E Divya Shanmugam %E Shengpu Tang %E Mercy Nyamewaa Asiedu %E Serina Chang %E Tom Hartvigsen %E Harvineet Singh %F pmlr-v225-odnoblyudova23a %I PMLR %P 428--444 %U https://proceedings.mlr.press/v225/odnoblyudova23a.html %V 225 %X 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.
APA
Odnoblyudova, A., Hizli, C., John, S., Cognolato, A., Juuti, A., Särkkä, S., Pietiläinen, K. & Marttinen, P.. (2023). Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. Proceedings of the 3rd Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 225:428-444 Available from https://proceedings.mlr.press/v225/odnoblyudova23a.html.

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