In patients with type 2 diabetes (T2D), accurate prediction of hypoglycemic events is crucial for maintaining glycemic control and reducing their frequency. However, individuals with high blood glucose variability experience significant fluctuations over time, posing a challenge for early warning models that rely on static features. This article proposes a novel hypoglycemia early alarm framework based on dynamic feature selection. The framework incorporates domain knowledge and introduces multi-scale blood glucose features, including predicted values, essential for early warnings. To address the complexity of the feature matrix, a dynamic feature selection mechanism (Relief-SVM-RFE) is designed to effectively eliminate redundancy. Furthermore, the framework employs online updates for the random forest model, enhancing the learning of more relevant features. The effectiveness of the framework was evaluated using a clinical dataset. For T2D patients with a high coefficient of variation (CV), the framework achieved a sensitivity of 81.15% and specificity of 98.14%, accurately predicting most hypoglycemic events. Notably, the proposed method outperformed other existing approaches. These results indicate the feasibility of anticipating hypoglycemic events in T2D patients with high CV using this innovative framework.

中文翻译：

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一种针对高血糖变异性 2 型糖尿病的新型低血糖警报框架

对于 2 型糖尿病 (T2D) 患者，准确预测低血糖事件对于维持血糖控制和降低其发生频率至关重要。然而，血糖变异性高的个体随着时间的推移会经历显着的波动，这对依赖静态特征的早期预警模型提出了挑战。本文提出了一种基于动态特征选择的新型低血糖早期报警框架。该框架融合了领域知识，并引入了多尺度血糖特征，包括对于早期预警至关重要的预测值。针对特征矩阵的复杂性，设计了动态特征选择机制（Relief-SVM-RFE）来有效消除冗余。此外，该框架采用随机森林模型的在线更新，增强了更多相关特征的学习。使用临床数据集评估该框架的有效性。对于变异系数 (CV) 高的 T2D 患者，该框架实现了 81.15% 的敏感性和 98.14% 的特异性，准确预测了大多数低血糖事件。值得注意的是，所提出的方法优于其他现有方法。这些结果表明使用这一创新框架预测高 CV 的 T2D 患者低血糖事件的可行性。