Development of a Neural Network for Prediction of Glucose Concentration in Type 1 Diabetes Patients

Pappada, Scott; Cameron, Brent D.; Rosman, Paul M.

doi:10.1177/193229680800200507

Cited by 108 publications

(88 citation statements)

References 27 publications

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“…Pappada and colleagues 59 used an ANN, based on CGM and additional patient diary information (meals and insulin infusion), to predict glucose values; they do not propose hypoglycemic prediction alarms, however. A number of different structures and training procedures can be used for artificial neural networks.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Bequette

2010

Journal of Diabetes Science and Technology

167

110

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Bequette

2010

Journal of Diabetes Science and Technology

167

110

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“…Pappada et al [14] presented a NNM trained with data from the CGMS Medtronic System in combination with other data manually recorded by the patient in an electronic diary (i.e. CBGM, insulin dosages, carbohydrate intakes, hypoglycemic and hyperglycemic symptoms, lifestyle activities, events and emotional state).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Pérez-Gandía

Facchinetti

Sparacino

et al. 2010

Diabetes Technology & Therapeutics

266

177

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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 paper proposes a new on-line 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 minutes, 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 (9 subjects using the Medtronic Guardian and 6 subjects using the Abbott Navigator). Three different PH are used, i.e. 15, 30 and 45 minutes. 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 minutes of PH, respectively. The prediction delay is around 4, 9 and 14 minutes for upward trends and 5, 15 and 26 minutes for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM) [1], has been performed. The comparison shows that, the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay. Conclusions:The proposed NNM is a reliable solution for the on-line prediction of future glucose concentrations from CGM data.

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“…The input u i is updated at each new sample time by the glucose reading from the CGM sensor, this means that the prediction is based on the most recent N readings of the glucose concentration. For example, if the PH is 25 minutes, and the input is u 6 [i.e. G 6 ], then the predicted glucose will be y 6 which is an estimate for G 11 .…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Gani et al, [5] developed a glucose prediction autoregressive (AR) model of order 30 to make short term, 30-minutes ahead prediction time, without time lag. Pappada et al, [6] used a dataset of different patients obtained by CGM to construct a neural network using NeuroSolutions® software to predict glucose concentration while time varying predictive window from 50-180 minutes is used. In this paper, we present a new prediction algorithm based on a RNN.…”

Section: Introductionmentioning

confidence: 99%

A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients

Allam

Nossai

Gomma

et al. 2011

IFIP Advances in Information and Communication Technology

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Abstract. Estimation of future glucose concentration is important for diabetes management. To develop a model predictive control (MPC) system that measures the glucose concentration and automatically inject the amount of insulin needed to keep the glucose level within its normal range, the accuracy of the predicted glucose level and the longer prediction time are major factors affecting the performance of the control system. The predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. In this article a new technique, which uses a recurrent neural network (RNN) and data obtained from CGM device, is proposed to predict the future values of the glucose concentration for prediction horizons (PH) of 15, 30, 45, 60 minutes. The results of the proposed technique is evaluated and compared relative to that obtained from a feed forward neural network prediction model (NNM). Our results indicate that, the RNN is better in prediction than the NNM for the relatively long prediction horizons.

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Development of a Neural Network for Prediction of Glucose Concentration in Type 1 Diabetes Patients

Cited by 108 publications

References 27 publications

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients

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