College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
Department of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai, 200040, China.
BMC Med Inform Decis Mak. 2021 Jan 21;21(1):22. doi: 10.1186/s12911-021-01389-x.
Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to diabetic treatment. In this study, we designed a multi-level hypoglycemia early alarm system to mine potential information in Continuous Glucose Monitoring (CGM) time series and improve the overall alarm performance for different clinical situations.
Through symbolizing the historical CGM records, the Prefix Span was adopted to obtain the early alarm/non-alarm frequent sequence libraries of hypoglycemia events. The longest common subsequence was used to remove the common frequent sequence for achieving the hypoglycemia early alarm in different clinical situations. Then, the frequent sequence pattern libraries with different risk thresholds were designed as the core module of the proposed multi-level hypoglycemia early alarm system.
The model was able to predict hypoglycemia events in the clinical dataset of level-I (sensitivity 85.90%, false-positive 23.86%, miss alarm rate 14.10%, average early alarm time 20.61 min), level-II (sensitivity 80.36%, false-positive 17.37%, miss alarm rate 19.63%, average early alarm time 27.66 min), and level-III (sensitivity 78.07%, false-positive 13.59%, miss alarm rate 21.93%, average early alarm time 33.80 min), respectively.
The proposed approach could effectively predict hypoglycemia events based on different risk thresholds to meet different prevention and treatment requirements. Moreover, the experimental results confirm the practicality and prospects of the proposed early alarm system, which reflects further significance in personalized medicine for hypoglycemia prevention.
低血糖的早期预警、无症状性低血糖的检测以及血糖波动的有效控制对糖尿病的治疗有重要贡献。在本研究中，我们设计了一个多层次的低血糖早期预警系统，以挖掘连续血糖监测（CGM）时间序列中的潜在信息，并提高不同临床情况下的整体预警性能。
通过对历史 CGM 记录进行符号化，采用前缀跨度（Prefix Span）获取低血糖事件的早期预警/非预警频繁序列库。使用最长公共子序列（Longest Common Subsequence）去除常见的频繁序列，以实现不同临床情况下的低血糖早期预警。然后，设计具有不同风险阈值的频繁序列模式库作为提出的多层次低血糖早期预警系统的核心模块。
该模型能够预测 I 级（灵敏度 85.90%，假阳性率 23.86%，漏报率 14.10%，平均早期预警时间 20.61 分钟）、II 级（灵敏度 80.36%，假阳性率 17.37%，漏报率 19.63%，平均早期预警时间 27.66 分钟）和 III 级（灵敏度 78.07%，假阳性率 13.59%，漏报率 21.93%，平均早期预警时间 33.80 分钟）临床数据集的低血糖事件。
该方法可以基于不同的风险阈值有效预测低血糖事件，以满足不同的预防和治疗需求。此外，实验结果证实了所提出的早期预警系统的实用性和前景，这在低血糖预防的个性化医疗方面具有进一步的意义。
