Read e-book online Artificial Intelligence in Medicine: 9th Conference on PDF

February 15, 2018 | Medicine | By admin | 0 Comments

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

ISBN-10: 3540201297

ISBN-13: 9783540201298

ISBN-10: 3540399070

ISBN-13: 9783540399070

This ebook constitutes the refereed complaints 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 offered including invited contributions have been conscientiously reviewed and chosen from sixty five submissions. The papers are geared up in topical sections on temporal reasoning, ontology and terminology, snapshot processing and simulation, directions and medical protocols, terminology and normal language matters, laptop studying, probabilistic networks and Bayesian types, case-based reasoning and determination help, and information mining and data discovery.

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Read or Download Artificial Intelligence in Medicine: 9th Conference on Artificial Intelligence, in Medicine in Europe, AIME 2003, Protaras, Cyprus, October 18-22, 2003. Proceedings PDF

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Extra info for Artificial Intelligence in Medicine: 9th Conference on Artificial Intelligence, in Medicine in Europe, AIME 2003, Protaras, Cyprus, October 18-22, 2003. Proceedings

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In this paper, we propose a semistructured data model for representing multimedia and temporal clinical information. Motivations are provided, taken from the domain of cardiac angiography. 1 Introduction During the last years, the amount of multimedia clinical data available electronically has been growing up [3]. Data resides in different form and this information is accessible through different interfaces like Web browsers, database query languages, application-specific interfaces or data exchange formats.

The MTGM database. The Presentation node is connected by means of an edge to the first media object of the presentation. The valid time of the Presentation node represents the time interval in which the presentation has been visualized. For example the presentation named “P1” reported in Figure 2 has been visualized by the physician in the interval [25/09/02 11:20, 25/09/02 11:25). The node Presentation is depicted with thick lines as the edge between the node Presentation itself and the Patient node.

In: Flahault, A. et al. ): Abstracts of 3rd International Workshop on Geography and Medicine, Paris (2001) 17 8. 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.

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Artificial Intelligence in Medicine: 9th Conference on Artificial Intelligence, in Medicine in Europe, AIME 2003, Protaras, Cyprus, October 18-22, 2003. Proceedings by Sylvie Charbonnier (auth.), Michel Dojat, Elpida T. Keravnou, Pedro Barahona (eds.)


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