By Sylvie Charbonnier (auth.), Michel Dojat, Elpida T. Keravnou, Pedro Barahona (eds.)
This publication constitutes the refereed lawsuits of the ninth convention on man made Intelligence in medication in Europe, AIME 2003, held in Protaras, Cyprus, in October 2003.
The 24 revised complete papers and 26 revised brief papers provided 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, picture processing and simulation, guidance and medical protocols, terminology and average language concerns, desktop studying, probabilistic networks and Bayesian types, case-based reasoning and determination aid, and knowledge mining and data discovery.
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Additional resources for Artificial Intelligence in Medicine: 9th Conference on Artificial Intelligence, in Medicine in Europe, AIME 2003, Protaras, Cyprus, October 18-22, 2003. Proceedings
LNAI 2394, Springer Berlin (2002) 99-107 3. : A Framework for Knowledge-Based Temporal Abstraction. Artificial Intelligence 90 (1997) 79-133 4. : Case-Based Reasoning: foundation issues. Methodological variation- and system approaches. AI Communications 7 (1) (1994) 39-59 5. L. : The effectiveness of Vaccination against Influenza in Adults. New England Journal of Medicine 333 (1995) 889-893 6. : Informed Consent Nelson-Hall, Inc. Chicago, III 7. Prou, M. : Exploratory Temporal-Spatial Analysis of Influenza Epidemics in France.
We intend to add more explicit data dependencies (as in a truth-maintenance system) to directly provide the data instances from which each fact was abstracted. • We will explore the use of graphical metaphors, both for display of existing periodic and linear patterns, as needed in the KNAVE-II system, and for specification of new patterns, either in the knowledge acquisition tool, or as part of an interactive application that enables the user to formulate a new query. We intend to gain insights from previous research done by Combi and Chitaro .
For the decision to warn, we split the list of the most similar courses in two lists. One list contains courses where a warning was appropriate; the second list gets the other ones. For both of these new lists we compute their sums of the reciprocal distances of their courses to get sums of similarities. Subsequently, the decision about the appropriateness of a warning depends on the question: which of these two sums is bigger. 3 First Results Our program computes early warnings of approaching influenza waves for the German federal state Mecklenburg-Western Pomerania.