GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Chengzhe Piao¹, Taiyu Zhu², Stephanie E Baldeweg³, Paul Taylor⁴, Pantelis Georgiou⁵, Jiahao Sun⁶, Jun Wang⁷, Kezhi Li⁸

¹Institute of Health Informatics, University College London, London, NW1 2DA, UK. Electronic address: chengzhe.piao.21@ucl.ac.uk.

²Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK. Electronic address: taiyu.zhu@psych.ox.ac.uk.

³Department of Diabetes & Endocrinology, University College London Hospitals, London, NW1 2PG, UK; Centre for Obesity & Metabolism, Department of Experimental & Translational Medicine, University College London, London, WC1E 6JF, UK. Electronic address: s.baldeweg@ucl.ac.uk.

⁴Institute of Health Informatics, University College London, London, NW1 2DA, UK. Electronic address: p.taylor@ucl.ac.uk.

⁵Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK. Electronic address: pantelis@imperial.ac.uk.

⁶FLock.io, London, WC2H 9JQ, UK. Electronic address: sun@flock.io.

⁷Department of Computer Science, University College London, London, WC1E 6EA, UK. Electronic address: junwang@cs.ucl.ac.uk.

⁸Institute of Health Informatics, University College London, London, NW1 2DA, UK. Electronic address: ken.li@ucl.ac.uk.

PubMed DOI

Abstract

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multimodal data, i.e., sensor data and self-reported event data, organized as multi-varia…

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