Deep prediction model: The case of online adaptive prediction of subcutaneous glucose

Jankovic, Marko; Mosimann, Samuel; Bally, Lia; Stettler, Christoph; Mougiakakou, Stavroula (29 December 2016). Deep prediction model: The case of online adaptive prediction of subcutaneous glucose. In: 13th Symposium on Neural Networks and Applications (NEUREL) (pp. 1-5). IEEE 10.1109/NEUREL.2016.7800095

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In this paper, we propose the concept of the deep prediction model for subcutaneous glucose concentration. The concept is based on several layers of prediction models. One aim of this approach is to eliminate time lag, which is more severe in longer prediction horizons. Thus, the prediction accuracy of the algorithm might be increased, even for longer prediction horizons. The second goal is to create new, potentially good predictors that could be obtained by combining existing predictors. The effectiveness of the proposed model is illustrated in several examples of two-layer networks. In the first layer, a specific linear/non-linear prediction model is used. In the second (correction) layer, an extreme learning machine is used, due to its rapid learning capabilities. In almost all experiments, the proposed method has reduced the time lag and improved the accuracy of the method.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition 04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
UniBE Contributor:	Jankovic, Marko, Bally, Lia Claudia, Stettler, Christoph, Mougiakakou, Stavroula
Subjects:	600 Technology > 610 Medicine & health 600 Technology > 620 Engineering
Publisher:	IEEE
Language:	English
Submitter:	Stavroula Mougiakakou
Date Deposited:	27 Mar 2017 15:54
Last Modified:	05 Dec 2022 15:01
Publisher DOI:	10.1109/NEUREL.2016.7800095
URI:	https://boris.unibe.ch/id/eprint/92893