Yu Xia, Yang Zi, Wang Xinzhuo, Sun Xiaoyu, Shen Ruiting, Li Hongru, Zhang Mingchen
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
Department of Endocrinology and Metabolism, Ningbo No.2 Hospital, Ningbo, Zhejiang Province, 315010, China.
BMC Med Inform Decis Mak. 2024 Dec 18;24(1):378. doi: 10.1186/s12911-024-02761-3.
Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source heterogeneous data. In this paper, a deep learning framework with an innovative dynamic attention mechanism is proposed to predict nocturnal hypoglycemic events for type 1 diabetes patients. Features related to nocturnal hypoglycemia are extracted from multi-scale and multi-dimensional data, which enables comprehensive information extraction from diverse sources. Then, we propose a prior-knowledge-guided attention mechanism to enhance the network's learning capability and interpretability. The method was evaluated on a public available clinical dataset, which successfully warned 94.91% of nocturnal hypoglycemic events with an F1-score of 96.35%. By integrating our proposed framework into the nocturnal hypoglycemia early warning model, issues related to feature redundancy and incompleteness were mitigated. Comparative analysis demonstrates that our method outperforms existing approaches, offering superior accuracy and practicality in real-world scenarios.
夜间低血糖是糖尿病患者面临的一个关键问题。未能及时干预对患者来说可能很危险。现有的早期预警方法难以从复杂的多源异构数据中全面提取关键信息。本文提出了一种具有创新动态注意力机制的深度学习框架，用于预测1型糖尿病患者的夜间低血糖事件。从多尺度和多维度数据中提取与夜间低血糖相关的特征，从而能够从不同来源全面提取信息。然后，我们提出了一种先验知识引导的注意力机制，以增强网络的学习能力和可解释性。该方法在一个公开可用的临床数据集上进行了评估，成功预警了94.91%的夜间低血糖事件，F1分数为96.35%。通过将我们提出的框架集成到夜间低血糖早期预警模型中，缓解了与特征冗余和不完整性相关的问题。对比分析表明，我们的方法优于现有方法，在实际场景中具有更高的准确性和实用性。
