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Multi-Horizon Glucose Prediction Across Populations with Deep Domain Generalization

Zhu, T; Afentakis, I; Li, K; Armiger, R; Hill, N; Oliver, N; Georgiou, P; (2024) Multi-Horizon Glucose Prediction Across Populations with Deep Domain Generalization. IEEE Journal of Biomedical and Health Informatics pp. 1-14. 10.1109/JBHI.2024.3428921. (In press). Green open access

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Abstract

Real-time continuous glucose monitoring (CGM), augmented with accurate glucose prediction, offers an effective strategy for maintaining blood glucose levels within a therapeutically appropriate range. This is particularly crucial for individuals with type 1 diabetes (T1D) who require long-term self-management. However, with extensive glycemic variability, developing a prediction algorithm applicable across diverse populations remains a significant challenge. Leveraging meta-learning for domain generalization, we propose GPFormer, a Transformer-based zero-shot learning method designed for multi-horizon glucose prediction. We developed GPFormer on the REPLACE-BG dataset, comprising 226 participants with T1D, and proceeded to evaluate its performance using three external clinical datasets with CGM data. These included the OhioT1DM dataset, a publicly available dataset including 12 T1D participants, as well as two proprietary datasets. The first proprietary dataset included 22 participants, while the second contained 45 participants, encompassing a diverse group with T1D, type 2 diabetes, and those without diabetes, including patients admitted to hospitals. These four datasets include both outpatient and inpatient settings, various intervention strategies, and demographic variability, which effectively reflect real-world scenarios of CGM usage. When compared with a group of machine learning baseline methods, GPFormer consistently demonstrated superior performance and achieved the lowest root mean square error for all the evaluated datasets up to a prediction horizon of two hours. These experimental results highlight the effectiveness and generalizability of the proposed model across a variety of populations, demonstrating its substantial potential to enhance glucose management in a wide range of practical clinical settings.

Type: Article
Title: Multi-Horizon Glucose Prediction Across Populations with Deep Domain Generalization
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JBHI.2024.3428921
Publisher version: http://dx.doi.org/10.1109/jbhi.2024.3428921
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Deep learning, diabetes, domain generalization, glucose prediction, Transformer
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10197097
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