Download Artificial Intelligence in Medicine: 11th Conference on by Thomas Guyet, Catherine Garbay, Michel Dojat (auth.), PDF

By Thomas Guyet, Catherine Garbay, Michel Dojat (auth.), Riccardo Bellazzi, Ameen Abu-Hanna, Jim Hunter (eds.)

This e-book constitutes the refereed complaints of the eleventh convention on man made Intelligence in drugs in Europe, AIME 2007, held in Amsterdam, The Netherlands in July 2007.

The 28 revised complete papers and 38 revised brief papers offered have been rigorously reviewed and chosen from 137 submissions. The papers are geared up in topical sections on agent-based platforms, temporal info mining, computing device studying and information discovery, textual content mining, common language processing and iteration, ontologies, choice help platforms, purposes of AI-based photo processing recommendations, protocols and instructions, in addition to workflow systems.

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Extra info for Artificial Intelligence in Medicine: 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, The Netherlands, July 7-11, 2007. Proceedings

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The evaluation shows competitive results compared to other algorithms with lower computational time. Indeed, local easy-to-segment territories converge quickly, allowing the system to focus on other areas. In addition, it appears to be robust to the intensity nonuniformity without any bias field assumption and estimation. Note that we currently consider a regular cubic partitioning for tissue segmentation but should in future work evaluate the contribution of more particular partitionning, like adaptive or spherical partitionings.

47–92. JAI Press, W. B. Rouse. Greenwich, CT (1989) 12. : Cognitive psychology and team training: Training shared mental models and complex systems. Human Factors Society Bulletin 33, 1–4 (1990) 13. : The NeoCITIES simulation: Understanding the design and experimental methodology used to develop a team emergency management simulation. In: Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, pp. edu Abstract. This paper presents the cognitive model of gastroesophageal reflux disease (GERD) developed for the Maryland Virtual Patient simulation and mentoring environment.

Therefore, we must develop DSS that incorporate context in a more meaningful way. Second, by improving the learning ability of DSSs, we can support decisionmaking in varying environments. Supporting the learning feature helps in identifying hidden associations in both explicit and implicit information that could be temporally and spatially distributed. This learning requirement necessitates DSSs to proactively synthesize new knowledge based on their ability to retain and recollect from past experiences.

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