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Continuous Learning Community
Continuous Learning Community
The Semantic Mining of Activity, Social and Health Data Project(SMASH)
THE CONCERN:Two thirds of the U.S. population is now overweight or obese. This results in significant health risks and financial costs to society. Support groups and other social reinforcement approaches have shown promise.
THE CONCEPT:The SMASH system will address a critical need for data mining tools to help understand the influence of health care social networks, such as YesiWell, on sustained weight loss using data which are multi-dimensional, historical, semantically diverse and very sensitive.
THE CONCEPT:System design and implementation rest on five specific aims. Resulting data resources, tools, ontologies and technologies will be made available to the larger research community upon completion.
aimlab.cs.uoregon.edu/SMASH/
Aims:1. To develop a novel data mining and statistical learning approach to
understand key factors that enable the spread of healthy behaviors in a social network. Our team will develop formal and expressive Semantic Web ontologies for the concepts used in describing the semantic features of health care data and social networks.
2. To bridge the domain knowledge in health care and social networks with formal mappings across ontological concepts.
3. To develop novel recommendation approaches building on top of influence modeling and prediction modeling. In addition, our team will develop methods to use the recommendation as a means to better organize the social network such that the adoption of optimal health behaviors in the network may spread quickly and sustainably.
4. To protect the privacy of human subjects during the data mining process for social network and health data, our team will provide tools for the enforcement of differential privacy through a privacy preserving analysis layer. We will develop novel solutions to preserve differential privacy for mining dynamic health data and social activities of human subjects.
5. To support this research, we will develop a web-accessible portal so that other researchers with little training in data mining will have shared access to data mining tools, ontologies and social network analysis results.
About PeaceHealth LaboratoriesPeaceHealth Laboratories is a division of PeaceHealth, a not-for-profit health care system with nearly 17,000 employees in Oregon, Washington and Alaska. With 11 laboratories and nearly 30 patient service centers in 3 states, PeaceHealth Laboratories is one of the largest regional laboratories in the Pacific Northwest www.peacehealthlabs.org
The Semantic Web is an extension of the World Wide Web. It provides a standardized way of expressing the relationships between web pages to allow machines to understand the meaning of hyperlinked information.
Ontology language for the Semantic Web describes information in terms of classes, properties, individuals and data values. Ontologies are stored as machine readable documents that can be based on a number of official syntactic variants.
*NIH/NIGMS #RO1GM103309A National Institutes of Health/National Institute of General Medical Sciences Award*
COMMUNITY PARTNERS: David Kil HealthMantic [email protected]
Consultant
Xintao Wu Professor; Data privacy laboratory director
University of North Carolina at Charlotte
Co-Investigator
SMASH
Dejing Dou Associate professor University of Oregon [email protected]
Principle Investigator
Junfeng Sun Mathematical statistician National Institutes of Health [email protected]
Co-Investigator
Jessica Greene Professor; Director of Research George Washington University [email protected]
Co-Investigator
Daniel Lowd Assistant professor University of Oregon [email protected]
Co-Investigator
Ruoming Jin Associate professor Kent State University [email protected]
Co-Investigator
Brigitte Piniewski, M.D. Chief Medical Officer PeaceHealth Laboratories [email protected]
Co-Investigator
Yelong Shen, Ruoming Jin, Dejing Dou, Nafisa Afrin Chowdhury, Junfeng Sun, Brigitte Piniewski, and David Kil. “Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network.” (To appear) Proceedings of the 12th IEEE International Conference on Data Mining (ICDM 2012) (6 pages short paper). Brussels, Belgium, December, 2012. (acceptance ratio: 20%, 78(full paper)+73(short paper)/756). URL: http://aimlab.cs.uoregon.edu/smash/papers/icdm12.pdf
Yue Wang, Xintao Wu, Jun Zhu, and Yang Xiang. “On Learning Cluster Coefficient from Private Networks.” In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM12). pp. 395-402, 2012. URL: http://aimlab.cs.uoregon.edu/SMASH/papers/asonam12.pdf
Jessica Greene, Rebecca Sacks, Brigitte Piniewski, David Kil, and Jin S. Hahn “The Impact of an Online Social Network with Wireless Monitoring Devices on Physical Activity and Weight Loss.” (Accepted by) Journal of Primary Care and Community Health, 2012. URL: http://aimlab.cs.uoregon.edu/smash/papers/JPCCH12.pdf
Brigitte Piniewski. “Personalized Medicine and Public Health.” in “Wireless Health: Remaking of Medicine by Pervasive Technologies.” edited by Mehran Mehergany, 2012. URL: http://aimlab.cs.uoregon.edu/smash/papers/book_chapter.pdf
14-LABO-040
