Abstract:
BACKGROUND:Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose. METHODS:A feed-forward NNM was designed for real-time prediction of glucose in patients with diabetes implementing a prediction horizon of 75 min. Inputs to the NNM included CGM values, insulin dosages, metered glucose values, nutritional intake, lifestyle, and emotional factors. Performance of the NNM was assessed in 10 patients not included in the model training set. RESULTS:The NNM had a root mean squared error of 43.9 mg/dL and a mean absolute difference percentage of 22.1. The NNM routinely overestimates hypoglycemic extremes, which can be attributed to the limited number of hypoglycemic reactions in the model training set. The model predicts 88.6% of normal glucose concentrations (> 70 and < 180 mg/dL), 72.6% of hyperglycemia (≥ 180 mg/dL), and 2.1% of hypoglycemia (≤ 70 mg/dL). Clarke Error Grid Analysis of model predictions indicated that 92.3% of predictions could be regarded as clinically acceptable and not leading to adverse therapeutic direction. Of these predicted values, 62.3% and 30.0% were located within Zones A and B, respectively, of the error grid. CONCLUSIONS:Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.
journal_name
Diabetes Technol Therjournal_title
Diabetes technology & therapeuticsauthors
Pappada SM,Cameron BD,Rosman PM,Bourey RE,Papadimos TJ,Olorunto W,Borst MJdoi
10.1089/dia.2010.0104subject
Has Abstractpub_date
2011-02-01 00:00:00pages
135-41issue
2eissn
1520-9156issn
1557-8593journal_volume
13pub_type
杂志文章abstract::Several biopharmaceuticals, including insulin and insulin analogs, are, or shortly will be, off-patent, thereby providing an opportunity for companies to attempt to manufacture "copies" commonly referred to as biosimilars and also known as follow-on biologics. Reassurance that such copy biologics are equally safe and ...
journal_title:Diabetes technology & therapeutics
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pub_type: 临床试验,杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章,多中心研究,随机对照试验
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pub_type: 杂志文章,评审
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abstract:BACKGROUND:Different reference methods are used for the accuracy assessment of continuous glucose monitoring (CGM) systems. The effect of using venous, arterialized-venous, or capillary reference measurements on CGM accuracy is unclear. METHODS:We evaluated 21 individuals with type 1 diabetes using a capillary calibra...
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pub_type: 杂志文章
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journal_title:Diabetes technology & therapeutics
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更新日期:2004-04-01 00:00:00
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pub_type: 杂志文章
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journal_title:Diabetes technology & therapeutics
pub_type: 杂志文章
doi:10.1089/dia.2012.0053
更新日期:2012-08-01 00:00:00
abstract:BACKGROUND:Continuous glucose monitoring (CGM) has been shown to be a valuable tool to improve glycemic control in patients with diabetes. The objective of this pilot study was to develop and implement CGM in an existing diabetes clinic for low-income patients on multiple daily injections. SUBJECTS AND METHODS:This wa...
journal_title:Diabetes technology & therapeutics
pub_type: 杂志文章,随机对照试验
doi:10.1089/dia.2013.0072
更新日期:2013-10-01 00:00:00
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pub_type: 杂志文章
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