Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

Sun, Qingnan; Jankovic, Marko; Bally, Lia; Mougiakakou, Stavroula Georgia (2018). Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network (In Press). In: 2018 14th IEEE Symposium on Neural Networks and Applications (NEUREL) - IEEE Neurel2018. Institute of Electrical and Electronics Engineers

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A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 retrospectively analysed datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition 04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition 04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
Graduate School:	Graduate School for Cellular and Biomedical Sciences (GCB)
UniBE Contributor:	Sun, Qingnan, Jankovic, Marko, Bally, Lia Claudia, Mougiakakou, Stavroula
Subjects:	600 Technology > 610 Medicine & health 600 Technology > 620 Engineering
Publisher:	Institute of Electrical and Electronics Engineers
Language:	English
Submitter:	Stavroula Mougiakakou
Date Deposited:	20 Sep 2018 08:24
Last Modified:	02 Mar 2023 23:31
ArXiv ID:	1809.03817
URI:	https://boris.unibe.ch/id/eprint/120024