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A multi-level hypoglycemia early alarm system based on sequence pattern mining

机译：基于序列模式挖掘的多级低血糖早期报警系统

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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.
机译：低血糖早期报警，无症状的低血糖检测，有效控制血糖波动对糖尿病治疗产生了巨大贡献。在这项研究中，我们设计了一种多级低血糖早期报警系统，用于连续葡萄糖监测（CGM）时间序列中的潜在信息，提高不同临床情况的整体报警性能。通过象征历史CGM记录，采用前缀跨度来获得低血糖事件的早期报警/非警报频繁序列文库。最长的常见随后用于去除在不同临床情况下实现低血糖早期报警的常见序列。然后，设计具有不同风险阈值的频繁序列模式库被设计为所提出的多级低血糖早期报警系统的核心模块。该模型能够预测水平-I临床数据集中的低血糖事件（敏感性85.90％，假阳性23.86％，未命中报警率为14.10％，平均早期报警时间20.61？min），II级（敏感度80.36％，假阳性17.37％，小姐报警率为19.63％，平均早期报警时间27.66？min），和第III级（敏感度78.07％，假阳性13.59％，误报率为21.93％，平均早期报警时间为33.80？分钟， 分别。该方法可以基于不同的风险阈值有效地预测低血糖事件，以满足不同的预防和治疗要求。此外，实验结果证实了提出的早期报警系统的实用性和前景，这反映了个性化药物的进一步意义，用于防血糖预防。

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