Dynamic Task Optimization in Remote Diabetes Monitoring Systems.
Dynamic Task Optimization in Remote Diabetes Monitoring Systems.
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DOI：
10.1109/hisb.2012.10
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Sarrafzadeh M
中科院分区：
文献类型：
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作者：
Suh MK;Woodbridge J;Moin T;Lan M;Alshurafa N;Samy L;Mortazavi B;Ghasemzadeh H;Bui A;Ahmadi S;Sarrafzadeh M
糖尿病是美国第七大死亡原因，但仔细的症状监测可以预防不良事件。实时患者监测和反馈系统是帮助糖尿病患者及其医疗保健专业人员监测健康相关测量并提供动态反馈的解决方案之一。然而，数据驱动的方法来动态地确定优先级和生成任务没有很好地研究在远程健康监测领域。本文介绍了一个无线健康项目（WANDA），利用传感器技术和无线通信来监测糖尿病患者的健康状况。WANDA动态任务管理功能实时应用数据分析来离散化连续特征，应用数据聚类和关联规则挖掘技术来动态管理滑动窗口大小，并对所需的用户任务进行优先级排序。所开发的算法最大限度地减少糖尿病患者使用关联规则，满足最低支持，置信度和条件概率阈值所需的日常行动项目的数量。这些任务中的每一项都能最大限度地获得信息，从而提高患者依从性和满意度的总体水平。实验结果表明，该算法可以预测进一步的事件具有较高的置信水平，并减少了高达76.19%的用户任务的数量。
Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.