Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events

Botwey, Ransford Henry; Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula

doi:10.1109/embc.2014.6944708

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…Moreover, the study investigated the performance improvement from the combined use of both RNN and AR with an output correction module. Moreover, Botwey et al [31] proposed combining an AR model with output correction and an RNN based on different data fusion schemes including the Dempster-Shafer evidential theory, GAs, and GP.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

124

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Background Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the differenc...

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Section: Discussionmentioning

confidence: 99%

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

124

View full text Add to dashboard Cite

show abstract

“…Finally, some teams 53,59,62,71,73,78,81,83 merged different algorithms having the same output to increase the models' performance, taking advantage of each algorithms characteristics. Vehi et al 87 took this strategy one step further, by proposing three algorithms for each situation, one predicting hypoglycaemia in the next hour using grammatical evolution, which can be enhanced by another algorithm for postprandial hypoglycaemia risk in the next 4 h following the meal using SVM, and another algorithm trained for night-time hypoglycaemia using ANN.…”

Section: Prediction Algorithms and Outputsmentioning

confidence: 99%

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Diouri

Cigler

Vettoretti

et al. 2021

Diabetes Metabolism Res

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The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

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“…Wang et al proposed the adaptive-weightedaverage framework that combines glucose predictive models by weighing them based on their past errors [11]. Daskalaki et al built a hypoglycemia/hyperglycemia events warning system by combining autoregressive and recurrent neural networks models [12], [13]. More recently, Jankovic et al studied a multi-step fusion methodology using ELM models for longterm glucose prediction [14].…”

Section: Introductionmentioning

confidence: 99%

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

Bois

Ammi²,

Yacoubi

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 in-silico type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.

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Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events

Cited by 13 publications

References 15 publications

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

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