Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction – A systematic literature review

Felizardo, Virginie; García, Nuno M.; Megdiche, Imen

doi:10.1016/j.artmed.2021.102120

Cited by 46 publications

(40 citation statements)

References 66 publications

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“…A comparison of different approaches with a prediction horizon of 30 min is given in the discussion summarized in Table 11. A more detailed table can be found in [37,42]. Further discussion regarding potential, challenges and concerns (used dataset, external validation) of AI in signal post-processing can be found in Section 5.2.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, machine learning can even enhance the diagnosis as well as the therapy of diabetes by improving the prediction of BGL trends, thus reducing hyperglycemia and hypoglycemia [36,37,41,42]. Furthermore, the risk of getting secondary diseases can be estimated [39,40].…”

Section: Post-processingmentioning

confidence: 99%

“…However, the main focus on using AI in post-processing of BGL data lies in predicting glucose trends [36]. Thereby, the prediction horizon of the BGL is up to 120 min [37], being based on data-based and hybrid models (e.g., Gaussian process and random forest [38]). Various features such as BGL, insulin, meal, exercise, sleep and others can be observed individually and combined with each other to improve prediction accuracy [37].…”

mentioning

confidence: 99%

“…Thereby, the prediction horizon of the BGL is up to 120 min [37], being based on data-based and hybrid models (e.g., Gaussian process and random forest [38]). Various features such as BGL, insulin, meal, exercise, sleep and others can be observed individually and combined with each other to improve prediction accuracy [37]. Additionally, AI-based approaches are also investigated for predicting the risk of secondary diseases [39,40].…”

mentioning

confidence: 99%

“…Additionally, AI-based approaches are also investigated for predicting the risk of secondary diseases [39,40]. However, this is out of the scope of the proposed paper, and detailed information can be found in the review papers [36,37,41,42].…”

mentioning

confidence: 99%

See 4 more Smart Citations

Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review

Xue

Thalmayer

Zeising

et al. 2022

Sensors

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Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

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Section: Discussionmentioning

confidence: 99%