Background and aim

Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction.

Methods

This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed.

Results

Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results.

Conclusions

The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic.

中文翻译：

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使用糖尿病患者真实数据进行血糖和低血糖预测的基于数据的算法和模型——系统文献综述

背景与目标

低血糖预测在糖尿病管理中发挥着重要作用，能够减少危险情况的数量。因此，有必要对当前可用的低血糖（或美国英语中的低血糖）预测算法和模型进行系统评价。

方法

本研究旨在系统地回顾有关使用糖尿病患者真实数据进行低血糖预测的基于数据的算法和模型的文献。对 2014 年 1 月至 2020 年 6 月发表的研究筛选了五个电子数据库：ScienceDirect、IEEE Xplore、ACM 数字图书馆、SCOPUS 和 PubMed。

结果

检索到符合纳入标准的 63 项符合条件的研究。该评价确定了该主题的当前趋势：大多数研究进行短期预测（82.5%）。此外，该评论指出了输入，并表明信息融合与低血糖预测相关。关于基于数据的模型 (80.9%) 和混合模型 (19.1%)，使用了不同的预测技术：人工神经网络 (22.2%)、集成学习 (27.0%)、监督学习 (20.6%)、统计/概率 (7.9%) )、自回归 (7.9%)、进化 (6.4%)、深度学习 (4.8%) 和自适应滤波器 (3.2%)。人工神经网络和混合模型显示出更好的结果。

结论

基于数据的血糖和低血糖预测模型应该能够在适用性和性能之间提供良好的平衡，整合来自不同来源或不同模型的补充数据。本综述确定了该主题研究的趋势和可能的机会。