This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient‐specific supervised‐machine‐learning (SML) analysis of glucose level based on a continuous‐glucose‐monitoring system (CGM) that needs no human intervention, and minimises false‐positive alerts. The CGM data over 7 to 50 non‐consecutive days from 11 type‐1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best‐fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root‐mean‐square‐error was 20.48 mg/dL and the average absolute‐mean‐error was 15.36 mg/dL when the best‐fit model was selected for each patient. Using the best‐fit‐model, the true‐positive‐hypoglycemia‐prediction‐rate was 64%, whereas the false‐positive‐ rate was 4.0%, and the false‐negative‐rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true‐positive‐hyperglycemia‐prediction‐rate was 61%. State‐of‐the‐art SML tools are effective in predicting the glucose level values of patients with type‐1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop “artificial pancreas” system. The algorithm provides a personalised medical solution that can successfully identify the best‐fit method for each patient.

中文翻译：

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使用机器学习提高血糖水平的可预测性。

本研究旨在通过基于连续葡萄糖监测系统 (CGM) 的新型患者特定监督机器学习 (SML) 葡萄糖水平分析来提高血糖水平的可预测性和未来的低血糖和高血糖事件警报，该系统不需要人为干预，并最大限度地减少误报警报。使用四种 SML 模型分析了来自 11 名 18 至 39 岁的平均 HbA1C 为 7.5% ± 1.2% 的 1 型糖尿病患者在 7 至 50 天内的非连续 CGM 数据。该算法旨在为每个患者选择最合适的模型。计算了几个统计参数以汇总预测误差的大小。该算法提供的个性化解决方案可有效预测最后一次测量后 30 分钟的血糖水平。当为每位患者选择最佳拟合模型时，平均均方根误差为 20.48 mg/dL，平均绝对平均误差为 15.36 mg/dL。使用最佳拟合模型，真阳性低血糖预测率为 64%，而假阳性率为 4.0%，假阴性率为 0.015%。即使仅考虑低于 70 的 CGM 样本，也发现了类似的结果。真阳性高血糖预测率为 61%。最先进的 SML 工具可有效预测 1 型糖尿病患者的血糖水平值，并通知这些患者未来的低血糖和高血糖事件，从而改善血糖控制。该算法可用于改善基础胰岛素率和推注胰岛素的计算，适用于闭环“人工胰腺”系统。