Dynamic Task Optimization in Remote Diabetes Monitoring Systems

Abstract

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 %.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

“…Contreras et al developed a diabetes management system to integrate a series of AI models and tools with an engine to manage diabetes patient care flows [ 139 ]. Finally, Suh et al proposed a dynamic care flow system that applied data clustering together with rule mining techniques to prioritize required user tasks [ 140 ].…”

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (Preprint)

“…Contreras et al developed a diabetes management system to integrate a series of AI models and tools with an engine to manage diabetes patient care flows [139]. Finally, Suh et al proposed a dynamic care flow system that applied data clustering together with rule mining techniques to prioritize required user tasks [140]. Furthermore, tools have been developed to analyze clinical appointments, medication, and therapy adherence.…”

Remote patient monitoring

“…The purpose of these remote monitoring and telemedicine systems is to reduce the potential costs to patient care [1]. Furthermore, such systems [2,17,3,4,18,5,6] discuss the potential of improving patient care with extensive monitoring techniques, but lack a comprehensive cost analysis to validate the effectiveness of the monitoring techniques. In particular, the systems must provide information in a way to reduce the workload, providing potential savings [9,1], but do not explicitly denote the cost figures.…”