@article {Wadghiri2022,
	title = {Ensemble blood glucose prediction in diabetes mellitus: A review},
	journal = {Computers in Biology and Medicine},
	volume = {147},
	year = {2022},
	note = {cited By 0},
	abstract = {Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients{\textquoteright} context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {algorithm, Algorithms, Blood, Blood glucose, Blood glucose level, Data mining, Diabetes mellitus, Digital libraries, Economic and social effects, Ensemble methods, Forecasting, Glucose, glucose blood level, Glucose dynamics, human, Humans, Machine learning, Machine learning techniques, Machine-learning, Performance, Single models, Trade off},
	doi = {10.1016/j.compbiomed.2022.105674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132341147\&doi=10.1016\%2fj.compbiomed.2022.105674\&partnerID=40\&md5=05bd59c3726654494a4be346e4e6a682},
	author = {Wadghiri, M.Z. and Idri, A. and El Idrissi, T. and Hakkoum, H.}
}
@article {Benaida2022476,
	title = {Machine and~Deep Learning Predictive Techniques for~Blood Glucose Level},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {476-485},
	abstract = {Allowing diabetic patients to predict their BGL is an important task for self-management of their metabolic disease. This allows to avoid hypo or hyperglycaemia by taking appropriate actions. Currently, this is possible due to the development of machine and deep learning techniques which are successfully used in many prediction tasks. This paper evaluates and compares the performances of six ML/DL techniques to forecast BGL predictions; four DL techniques: CNN, LSTM, GRU, DBN and two ML/statistic techniques: SVR, and AR. The evaluation of the performance of the six regressors were in term of four criteria: RMSE, MAE, MMRE, and PRED. In addition, the Scott-Knott were used to evaluate the statistical significance test and to rank the regressors. The results show that AR was the best for 5~min ahead forecasting with a mean of RMSE equal to 8.67~mg/dl. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130238876\&doi=10.1007\%2f978-3-031-04826-5_48\&partnerID=40\&md5=8889c0cf2c91459b8f32389a846059f5},
	author = {Benaida, M. and Abnane, I. and Idri, A. and El Idrissi, T.}
}
@article {ElIdrissi2021347,
	title = {Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction},
	journal = {Communications in Computer and Information Science},
	volume = {1400 CCIS},
	year = {2021},
	note = {cited By 0},
	pages = {347-365},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30~min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Learning architectures, Learning techniques, Long short-term memory, Mean square error, Parameters selection, Root mean square errors},
	doi = {10.1007/978-3-030-72379-8_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107268637\&doi=10.1007\%2f978-3-030-72379-8_17\&partnerID=40\&md5=2b36d3e75ce8d317c8b80f3272f4d956},
	author = {El Idrissi, T. and Idri, A.}
}
@article {ElIdrissi2020379,
	title = {Deep Learning for Blood Glucose Prediction: CNN vs LSTM},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12250 LNCS},
	year = {2020},
	note = {cited By 6},
	pages = {379-393},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this study, we propose a Convolutional Neural Network (CNN) for BGL prediction. This CNN is compared with Long-short-term memory (LSTM) model for both one-step and multi-steps prediction. The objectives of this work are: 1) Determining the best configuration of the proposed CNN, 2) Determining the best strategy of multi-steps forecasting (MSF) using the obtained CNN for a prediction horizon of 30~min, and 3) Comparing the CNN and LSTM models for one-step and multi-steps prediction. Toward the first objective, we conducted series of experiments through parameter selection. Then five MSF strategies are developed for the CNN to reach the second objective. Finally, for the third objective, comparisons between CNN and LSTM models are conducted and assessed by the Wilcoxon statistical test. All the experiments were conducted using 10 patients{\textquoteright} datasets and the performance is evaluated through the Root Mean Square Error. The results show that the proposed CNN outperformed significantly the LSTM model for both one-step and multi-steps prediction and no MSF strategy outperforms the others for CNN. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Blood, Blood glucose, Blood glucose level, Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Long short-term memory, Mean square error, Multi-step, Parameter selection, Prediction horizon, Root mean square errors},
	doi = {10.1007/978-3-030-58802-1_28},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093089262\&doi=10.1007\%2f978-3-030-58802-1_28\&partnerID=40\&md5=f16d576a801dcdbbac35f8593e4a50ea},
	author = {El Idrissi, T. and Idri, A.}
}
@conference {ElIdrissi2020337,
	title = {Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: A comparative study},
	booktitle = {HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020},
	year = {2020},
	note = {cited By 3},
	pages = {337-344},
	abstract = {Predicting the blood glucose level (BGL) is crucial for self-management of Diabetes. In general, a BGL prediction is done based on the previous measurements of BGL, which can be taken either (manually) by using sticks or (automatically) by using continuous glucose monitoring (CGM) devices. To allow the diabetic patients to take appropriate actions, the BGL predictions should be done ahead of time; thus a multi-step ahead prediction is suitable. Therefore, many Multi-Step-ahead Forecasting (MSF) strategies have been developed and evaluated, and can be categorized in five types: Recursive, Direct, MIMO (for Multiple Input Multiple Output), DirMO (combining Direct and MIMO) and DirRec (combining Direct and Recursive). However, none of them is known to be the best strategy in all contexts. The present study aims at: 1) reviewing the MSF strategies, and 2) determining the best strategy to fit with a LSTM Neural Network model. Hence, we evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE), the five MSF strategies using a LSTM Neural Network with an horizon of 30 minutes. The results show that there is no strategy that significantly outperformed others when using the Wilcoxon statistical test. However, when using the Sum Ranking Differences method, MIMO is the best strategy for both RMSE and MAE criteria. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Forecasting, Glucose, Long short-term memory, Mean absolute error, Mean square error, Medical informatics, MIMO systems, Multi-step-ahead predictions, Neural network model, Performance criterion, Root mean square errors},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083722214\&partnerID=40\&md5=bd9e45a8538559492b02432e5567d10e},
	author = {El Idrissi, T. and Idri, A. and Kadi, I. and Bakkoury, Z.}
}
@article {ElIdrissi20181142,
	title = {Data mining techniques in diabetes self-management: A systematic map},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {746},
	year = {2018},
	pages = {1142-1152},
	doi = {10.1007/978-3-319-77712-2_109},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045346296\&doi=10.1007\%2f978-3-319-77712-2_109\&partnerID=40\&md5=627778cf3f0df5fe749b6e457a0eef1f},
	author = {El Idrissi, T. and Idri, A. and Bakkoury, Z.}
}
