Dynamic Task Optimization in Remote Diabetes Monitoring Systems.
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 %.
Computer Science Department, University of California, Los Angeles.
Myung-Kyung
MK
Suh
Computer Science Department, University of California, Los Angeles.
Jonathan
J
Woodbridge
Department of Medicine, Division of Endocrinology, University of California, Los Angeles; HSR&D Center of Excellence for the Study of Healthcare Provider Behavior, VA Greater Los Angeles.
Tannaz
T
Moin
Computer Science Department, University of California, Los Angeles.
Mars
M
Lan
Computer Science Department, University of California, Los Angeles.
Nabil
N
Alshurafa
Computer Science Department, University of California, Los Angeles.
Lauren
L
Samy
Computer Science Department, University of California, Los Angeles.
Bobak
B
Mortazavi
Computer Science Department, University of California, Los Angeles.
Hassan
H
Ghasemzadeh
Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles.
Alex
A
Bui
Department of Medicine, Division of Endocrinology, University of California, Los Angeles.
Sheila
S
Ahmadi
Computer Science Department, University of California, Los Angeles.
Majid
M
Sarrafzadeh
Apriori algorithm; association rule mining; diabetes; expectation maximization algorithm; real-time feedback; remote health monitoring; task optimization; telemedicine; wireless health