Dynamic Task Optimization in Remote Diabetes Monitoring Systems

Myung-kyung Suh; Jonathan Woodbridge; Tannaz Moin; Mars Lan; Nabil Alshurafa; Lauren Samy; Bobak Mortazavi; Hassan Ghasemzadeh; Alex Bui; Sheila Ahmadi; Majid Sarrafzadeh

doi:10.1109/HISB.2012.10

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Dynamic Task Optimization in Remote Diabetes Monitoring Systems

Journal article

Open access

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

Myung-kyung Suh, Jonathan Woodbridge, Tannaz Moin, Mars Lan, Nabil Alshurafa, Lauren Samy, Bobak Mortazavi, Hassan Ghasemzadeh, Alex Bui, Sheila Ahmadi, …

Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, Vol.2012, pp.3-11

09/2012

DOI: https://doi.org/10.1109/HISB.2012.10

Handle:

https://hdl.handle.net/2376/116140

PMCID: PMC5016191

PMID: 27617297

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Abstract

telemedicine

task optimization

wireless health

Apriori algorithm

remote health monitoring

expectation maximization algorithm

diabetes

real-time feedback

association rule mining

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 %.

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Details

Title: Dynamic Task Optimization in Remote Diabetes Monitoring Systems
Creators: Myung-kyung Suh - Computer Science Department, University of California, Los Angeles
Jonathan Woodbridge - Computer Science Department, University of California, Los Angeles
Tannaz Moin - Department of Medicine, Division of Endocrinology, University of California, Los Angeles
Mars Lan - Computer Science Department, University of California, Los Angeles
Nabil Alshurafa - Computer Science Department, University of California, Los Angeles
Lauren Samy - Computer Science Department, University of California, Los Angeles
Bobak Mortazavi - Computer Science Department, University of California, Los Angeles
Hassan Ghasemzadeh - Computer Science Department, University of California, Los Angeles
Alex Bui - Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles
Sheila Ahmadi - Department of Medicine, Division of Endocrinology, University of California, Los Angeles
Majid Sarrafzadeh - Computer Science Department, University of California, Los Angeles
Publication Details: Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, Vol.2012, pp.3-11
Academic Unit: Electrical Engineering and Computer Science, School of
Identifiers: 99900547302401842
Language: English
Resource Type: Journal article