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Correlating health and wellness analytics for personalized decision making


Khan, WA and Idris, M and Ali, T and Ali, R and Hussain, S and Hussain, M and Amin, MB and Khattak, AM and Weiwei, Y and Afzal, M and Lee, S and Kang, BH, Correlating health and wellness analytics for personalized decision making, Proceedings of 17th International Conference on E-health Networking, Application & Services, 14-17 October 2015, Boston, Massacheusetts, United States, pp. 256-261. ISBN 978-1-4673-8325-7 (2016) [Non Refereed Conference Paper]

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DOI: doi:10.1109/HealthCom.2015.7454508


Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:diseases, diabetes, cognition, sensors, knowledge based systems, decision making, media
Research Division:Health Sciences
Research Group:Health services and systems
Research Field:Health informatics and information systems
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Amin, MB (Dr Muhammad Bilal Amin)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:117025
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2017-05-29
Last Modified:2021-03-25

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