Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

Cañete, J. Fernández de; Gonzalez-Perez, S.; Ramos-Diaz, J. C.

doi:10.1016/j.cmpb.2011.11.006

Cited by 54 publications

(15 citation statements)

References 36 publications

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“…The main challenge of physiological models is the lack of generalization capability and need support from data for higher prediction performance. Data-driven approaches are mainly based on machine learning methods such as fuzzy logic and rule-based models [14], multi-modal approaches [15,16] autoregressive models [17,18], support vector machine [19] and artificial neural networks models [20]. The hybrid approach includes physiological models such as glucose digestion and absorption, insulin absorptions, exercise, and other events.…”

Section: Blood Glucose Prediction Researchmentioning

confidence: 99%

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

Wang¹,

Lewis³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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High accuracy of blood glucose prediction over the long term is essential for preventative diabetes management. The emerging closed-loop insulin delivery system such as the artificial pancreas system (APS) provides opportunities for improved glycaemic control for patients with type 1 diabetes. Existing blood glucose studies are proven effective only within 30 minutes but the accuracy deteriorates drastically when the prediction horizon increases to 45 minutes and 60 minutes. Deep learning, especially for long short term memory (LSTM) and its variants have recently been applied in various areas to achieve state-of-the-art results in tasks with complex time series data. In this study, we present deep LSTM based models that are capable of forecasting long term blood glucose levels with improved prediction and clinical accuracy. We evaluate our approach using 20 cases(878,000 glucose values) from Open Source Artificial Pancreas System (OpenAPS). On 30-minutes and 45-minutes prediction, our Stacked-LSTM achieved the best performance with Root-Mean-Square-Error (RMSE) marks 11.96 & 15.81 and Clark-Grid-ZoneA marks 0.887 & 0.784. In terms of 60-minutes prediction, our ConvLSTM has the best performance with RMSE = 19.6 and Clark-Grid-ZoneA=0.714. Our models outperform existing methods in both prediction and clinical accuracy. This research can hopefully support patients with type 1 diabetes to better manage their behavior in a more preventative way and can be used in future real APS context.

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Section: Blood Glucose Prediction Researchmentioning

confidence: 99%

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

Wang¹,

Lewis³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Advanced computational methods, including ANNs, utilize diverse types of input data that are processed in the context of previous training history on a defined sample database to produce a clinically relevant output, for example, the probability of a certain pathology or classification of biomedical objects. Due to the substantial plasticity of input data, ANNs have proven useful in the analysis of blood and urine samples of diabetic patients (see [4], [14]), diagnosis of tuberculosis (see [10], [11]), and leukemia classification [8].…”

Section: In Addition To Statistical Models Several Machine-learning mentioning

confidence: 99%

Artificial Neural Networks and Logistic Regression With an Ordered Categorical Response: Study Chronic Kidney Disease

2016

Jour. Stat. Adv. Theory and Appl.

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The health research has become increasingly reliant on statistical modelling techniques to assess the effect of new health programs, the impact of risk factors on disease, the effects of health behaviours, and a host of other health concerns. Clinical researchers conduct studies about diagnostic tests mainly for MONA N. ABDEL BARY et al. 68 the purpose of either estimating the diagnostic accuracy of a test according to different patient or environmental characteristics or comparing diagnostic accuracy of different tests according to different patient or environmental characteristics. Studies are required to develop robust statistical methods to analyze data from diagnostic studies and assess the properties of available statistical methods. Multiple analytical statistical methods are available to analyze ordinal data. These methods can be a model based approach, such as models for cumulative response probabilities or a non-model based approach, such as a nonparametric method based on ranking. A common model based method used to analyze ordinal data is an ordinal logistic regression. In addition to statistical models, several machine learning algorithms are also available to analyze ordinal data, such as an artificial neural network model, a decision tree model, and a support vector machine model. The current study compares the performance of ordinal logistic regression model with artificial neural networks models for prediction of chronic kidney disease. The results of the current study show outstanding performance of artificial neural networks models for the prediction of the level of chronic kidney disease. In addition, the study illustrates the most affected variables are gender, surgical operations, the blood pressure and potassium ratio. There are many previous studies focus on demonstrated the ability of ANN models in applications binary classifications but through the current study we use neural networks models in multiple classifications. The results of the current study show the following: successful of ANN models in the process of separating and classifying accuracy rate of almost 100%, and the ordinal regression model succeeded in identifying risk factors for renal failure moral influence on the regression model.

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“…ANN are data mining tools used fundamentally to look for patterns in training sets of data, then the procedure learns these patterns and develops the ability to classify new patterns correctly. [27][28][29][30][31][32] In this study, we used a feed-forward neural network trained by a back-propagation algorithm (multi-layer perceptron [MLP]). The MLP, one of the most commonly used neural network architectures, uses a feed-forward architecture and can have multiple hidden layers.…”

Section: Statistical Analysis: Neural Network Techniquementioning

confidence: 99%

Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

García‐Martín

Herrero

Bambo

et al. 2013

Seminars in Ophthalmology

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Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

Cited by 54 publications

References 36 publications

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

Artificial Neural Networks and Logistic Regression With an Ordered Categorical Response: Study Chronic Kidney Disease

Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

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