PCOPM: a probabilistic CBR framework for obesity prescription management

Authors:
Dong Dong;Zhaohao Sun;Feng Gao
Affiliations:
College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, China;Graduate School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Victoria, Australia;College of Physical Education, Hebei Normal University, Shijiazhuang, China
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

Obesity has become one of the most prevalent health problems around the world. Many obesity therapy cases need efficient management in order to be shared and utilized. Prescription management has been proved to be successful strategy in obesity management. Since a case usually contains incomplete information, this article examines probabilistic case-based reasoning (CBR) by integrating Bayesian networks (BN) with CBR and proposes a probabilistic CBR framework for obesity prescription management (PCOPM) to assist health professionals to share their experiences of obesity exercise prescription online. The PCOPM ties together CBR and BN into a unified framework that includes both obesity experience and intelligent embodiment of decision making for obesity management. The proposed approach will facilitate the research and development of intelligent web-based obesity management.