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

William Sandham; Dimitra Nikoletou; David Hamilton; Ken Paterson; Alan Japp; Catriona McGregor

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

William Sandham
, Dimitra Nikoletou
, David Hamilton
, Ken Paterson
, Alan Japp
, Catriona McGregor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing
Editors	Sergios Theodoridis
Place of Publication	Patras, Greece
Publication status	Published - 1998

Bibliographical note

Note: This paper was published in Theodoridis, Sergios,
(ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054

Organising Body: European Association for Signal Processing

Organising Body: European Association for Signal Processing

Keywords

Computer science and informatics

1 Paper

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network
Sandham, W., Nikoletou, D., Hamilton, D., Paterson, K., Japp, A. & McGregor, C., 1998.
Research output: Contribution to conference › Paper › peer-review

Cite this

Sandham, W., Nikoletou, D., Hamilton, D., Paterson, K., Japp, A., & McGregor, C. (1998). Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. In S. Theodoridis (Ed.), This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing

Sandham, William ; Nikoletou, Dimitra ; Hamilton, David et al. / Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing. editor / Sergios Theodoridis. Patras, Greece, 1998.

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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.",

keywords = "Computer science and informatics",

author = "William Sandham and Dimitra Nikoletou and David Hamilton and Ken Paterson and Alan Japp and Catriona McGregor",

note = "Note: This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing",

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Sandham, W, Nikoletou, D, Hamilton, D, Paterson, K, Japp, A & McGregor, C 1998, Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. in S Theodoridis (ed.), This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing. Patras, Greece.

Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. / Sandham, William; Nikoletou, Dimitra; Hamilton, David et al.
This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing. ed. / Sergios Theodoridis. Patras, Greece, 1998.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Blood glucose prediction for diabetes therapy using a recurrent artificial neural network

AU - Sandham, William

AU - Nikoletou, Dimitra

AU - Hamilton, David

AU - Paterson, Ken

AU - Japp, Alan

AU - McGregor, Catriona

N1 - Note: This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

KW - Computer science and informatics

M3 - Conference contribution

BT - This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing

A2 - Theodoridis, Sergios

CY - Patras, Greece

ER -

Sandham W, Nikoletou D, Hamilton D, Paterson K, Japp A, McGregor C. Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. In Theodoridis S, editor, This paper was published in Theodoridis, Sergios, (ed.) Signal processing IX : theories and applications. Patras, Greece : Typorama. pp. 673-676. ISBN: 9607620054 Organising Body: European Association for Signal Processing Organising Body: European Association for Signal Processing. Patras, Greece. 1998

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

Abstract

Bibliographical note

Keywords

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Research output

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

Cite this