Development of a neural network model for predicting glucose levels in a surgical critical care settingReport as inadecuate




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Patient Safety in Surgery

, 4:15

First Online: 09 September 2010Received: 22 May 2010Accepted: 09 September 2010

Abstract

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent-directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia.

Electronic supplementary materialThe online version of this article doi:10.1186-1754-9493-4-15 contains supplementary material, which is available to authorized users.

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Author: Scott M Pappada - Marilyn J Borst - Brent D Cameron - Raymond E Bourey - Jason D Lather - Desmond Shipp - Antonio Chir

Source: https://link.springer.com/article/10.1186/1754-9493-4-15



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