Ensemble blood glucose prediction in diabetes mellitus: A review

Wadghiri, Mohamed Zaim; Idri, Ali; Idrissi, Touria El; Hakkoum, Hajar

doi:10.1016/j.compbiomed.2022.105674

Cited by 26 publications

(12 citation statements)

References 46 publications

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“…Five deep learning models were used in Glu-Ensemble: Recurrent neural network (RNN), long short-term memory (LSTM), stack LSTM, bidirectional (Bi)LSTM, and gated recurrent unit (GRU). The aforementioned models are used for time-series forecasting in various fields and are known to have good forecasting performance [ [43] , [44] , [45] ]. An RNN is a neural network that has loops to enable information to persist over time and has been widely used in time-series forecasting tasks.…”

Section: Glu-ensemble Architecturementioning

confidence: 99%

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Han,

Kim,

Woo

et al. 2024

Heliyon

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Section: Glu-ensemble Architecturementioning

confidence: 99%

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Han,

Kim,

Woo

et al. 2024

Heliyon

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“…For further alignment with the contents of this study, the focus of this overview is on the application of state-of-the-art machine learning techniques and the use of Ohio type 1 diabetes datasets for model development and evaluation. A more comprehensive review of the latest revolutions in the blood glucose level prediction area can be studied at these references [ 51 , 52 , 53 , 54 ].…”

Section: Literature Surveymentioning

confidence: 99%

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion

et al. 2023

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Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis’s congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.

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“…Commonly used machine learning methods include the least absolute shrinkage and selection operator (LASSO), support vector machine (SVM) and random forest (RF). Each algorithm possesses unique variable filtering capabilities, and their combined application can effectively address the limitations of individual methods [ 13 , 14 ]. To enhance accuracy, it may be more effective to employ a combination of multiple methods when screening for diagnostic biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic plasma small extracellular vesicles miRNA signatures for pancreatic cancer using machine learning methods

Pu,

Zhang,

Ding

et al. 2024

Translational Oncology

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Ensemble blood glucose prediction in diabetes mellitus: A review

Cited by 26 publications

References 46 publications

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion

Diagnostic plasma small extracellular vesicles miRNA signatures for pancreatic cancer using machine learning methods

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