Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

Sandham, William, Nikoletou, Dimitra, Hamilton, David, Paterson, Ken, Japp, Alan and McGregor, Catriona (1998) Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. In: EUSIPCO-98: Ninth European Signal Processing Conference; 08-11 Sep 1998, Rhodes, Greece.

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Abstract

Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.

Item Type: Conference or Workshop Item (Paper)
Event Title: EUSIPCO-98: Ninth European Signal Processing Conference
Organising Body: European Association for Signal Processing
Additional Information: This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054
Research Area: Computer science and informatics
Health services research
Faculty, School or Research Centre: Faculty of Health and Social Care Sciences (until 2013)
Depositing User: Dimitra Nikoletou
Date Deposited: 28 Jul 2016 09:15
Last Modified: 28 Jul 2016 09:15
URI: http://eprints.kingston.ac.uk/id/eprint/25050

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