Download Artificial Intelligence in Medicine: 9th Conference on by Sylvie Charbonnier (auth.), Michel Dojat, Elpida T. PDF

By Sylvie Charbonnier (auth.), Michel Dojat, Elpida T. Keravnou, Pedro Barahona (eds.)

This e-book constitutes the refereed court cases of the ninth convention on synthetic Intelligence in medication in Europe, AIME 2003, held in Protaras, Cyprus, in October 2003.

The 24 revised complete papers and 26 revised brief papers awarded including invited contributions have been conscientiously reviewed and chosen from sixty five submissions. The papers are equipped in topical sections on temporal reasoning, ontology and terminology, snapshot processing and simulation, directions and medical protocols, terminology and common language matters, computing device studying, probabilistic networks and Bayesian types, case-based reasoning and choice aid, and knowledge mining and information discovery.

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References 1. S. Abiteboul, P. Buneman, and D. Suciu. Data on the Web: From Relations to Semistrucutred Data and XML. Morgan Kaufmann, 1999. 2. A. F. C´ ardenas and J. D. N. Dionisio. A Unified Data Model for Representing Multimedia, Timeline and Simulation Data. IEEE Transactions on Knowledge and Data Engeneering, 10(5):746–767, Sept-Oct 1998. 3. C. Combi, L. Portoni, and F. Pinciroli. User-oriented views in health care information systems. IEEE Transactions on Biomedical Engineering, 49(12):1387–1398, 2002.

Shindo, N. : Distribution of the Influenza Warning Map by Internet. In: Flahault, A. et al. ): Abstracts of 3rd International Workshop on Geography and Medicine, Paris (2001) 16 9. : The Detection of Outbreaks of Infectious Disease. In Gierl, L et al. ): GEOMED ’97, International Workshop on Geomedical Systems, Teubner Stuttgart (1997) 97-117 10. Viboud, C. : Forecasting the spatio-temporal spread of influenza epidemics by the method of analogues. In: Abstracts of 22nd Annual Conference of the International Society of Clinical Biostatistics, Stockholm, August 20-24 (2001) 71 11.

Intuitively, we introduce setting nodes (depicted as thick squares) to represent the information related to the visualization of a media object. rt 3 Description 2 Name 1 t0 5 t1 t2 t3 t Fig. 1. The timeline of the multimedia presentation with its mpis. For example, in the first part of the presentation the object Name can be shown on the top of the screen, while in the last one it can be visualized in the center of the screen. In order to recognize the different instances of the same media object in a given presentation, we introduce the concept of the media presentation identifier (mpi).

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