- Title
- PCOPM : A probabilistic CBR framework for obesity prescription management
- Creator
- Dong, Dong; Sun, Zhaohao; Gao, Feng
- Date
- 2010
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/69049
- Identifier
- vital:3633
- Identifier
-
https://doi.org/10.1007/978-3-642-14932-0_12
- Identifier
- ISBN:9783642149313
- 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. © 2010 Springer-Verlag Berlin Heidelberg.
- Publisher
- Changsha, China : Springer
- Relation
- Paper presented at 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China : 18th-21st August 2010 Vol. 6216 LNAI, p. 91-99
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian network; Case-based reasoning; Obesity management; Ontology
- Hits: 1111
- Visitors: 930
- Downloads: 1
| Thumbnail | File | Description | Size | Format |
|---|