Improving blood glucose level predictability using machine learning
Improving blood glucose level predictability using machine learning
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使用机器学习提高血糖水平的可预测性
DOI：
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发表时间：
2020
期刊：
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通讯作者：
Mira Gonen
中科院分区：
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作者：
Y. Marcus;R. Eldor;Mariana Yaron;S. Shaklai;M. Ish‐shalom;G. Shefer;N. Stern;Nehor Golan;A. Dvir;Ofir Pele;Mira Gonen
本研究旨在通过基于无需人为干预的连续葡萄糖监测系统（CGM）的新型患者特定监督机器学习（SML）葡萄糖水平分析来改善血糖水平可预测性和未来低血糖和高血糖事件警报，并最大限度地减少假阳性警报。使用4种SML模型分析了11例年龄为18 - 39岁、平均HbA 1C为7.5% ± 1.2%的1型糖尿病患者7 - 50个非连续日的CGM数据。构建算法以选择每例患者的最佳拟合模型。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。当为每例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样品，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。该算法可用于改进基础胰岛素速率和餐时胰岛素的计算，适用于闭环“人工胰腺”系统。该算法提供了一种个性化的医疗解决方案，可以成功地为每位患者确定最适合的方法。
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.