Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Pérez Gandía, Carmen; Facchinetti, A.; Sparacino, G.; Cobelli, C.; Gómez Aguilera, Enrique J.; Rigla Cros, Mercedes; Leiva, Alberto de y Hernando Pérez, María Elena (2010). Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.. "Diabetes Technology AND Therapeutics", v. 12 (n. 1); pp. 81-88. ISSN 1520-9156. https://doi.org/10.1089/dia.2009.0076.

Descripción

Título: Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
Autor/es:
  • Pérez Gandía, Carmen
  • Facchinetti, A.
  • Sparacino, G.
  • Cobelli, C.
  • Gómez Aguilera, Enrique J.
  • Rigla Cros, Mercedes
  • Leiva, Alberto de
  • Hernando Pérez, María Elena
Tipo de Documento: Artículo
Título de Revista/Publicación: Diabetes Technology AND Therapeutics
Fecha: Enero 2010
Volumen: 12
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay.

Más información

ID de Registro: 8586
Identificador DC: http://oa.upm.es/8586/
Identificador OAI: oai:oa.upm.es:8586
Identificador DOI: 10.1089/dia.2009.0076
URL Oficial: http://www.liebertonline.com/doi/abs/10.1089/dia.2009.0076
Depositado por: Memoria Investigacion
Depositado el: 10 Ago 2011 10:31
Ultima Modificación: 20 Abr 2016 17:19
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