Frank van Harmelen (auth.), Silvia Miksch, Jim Hunter,'s Artificial Intelligence in Medicine: 10th Conference on PDF

By Frank van Harmelen (auth.), Silvia Miksch, Jim Hunter, Elpida T. Keravnou (eds.)

This e-book constitutes the refereed lawsuits of the tenth convention on man made Intelligence in medication in Europe, AIME 2005, held in Aberdeen, united kingdom in July 2005.

The 35 revised complete papers and 34 revised brief papers offered including 2 invited contributions have been rigorously reviewed and chosen from 148 submissions. The papers are equipped in topical sections on temporal illustration and reasoning, determination help structures, scientific directions and protocols, ontology and terminology, case-based reasoning, sign interpretation, visible mining, laptop imaginative and prescient and imaging, wisdom administration, computer studying, wisdom discovery, and information mining.

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Read Online or Download Artificial Intelligence in Medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005. Proceedings PDF

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Additional resources for Artificial Intelligence in Medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005. Proceedings

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16. : Fast algorithms for mining association rules in large databases. In: Proceedings of the International Conference on Very Large Databases. Morgan Kaufmann (1994) 478-499 17. : A framework for knowledge-based temporal abstraction. Artificial Intelligence 90 (1997) 79-133 18. : Towards a general theory of action and time. Artificial Intelligence 23 (1984) 123-154 19. : Clustering gene expression data with temporal abstractions. In: Medinfo. (2004) 798-802 20. : Network dynamics and cell physiology.

This representation is based on the definition of complex temporal events, that are complex abstractions defined as the time intervals in which specific interesting 26 L. Sacchi et al. a) INPUT: raw data (biomedical time series) TA mechanism b) Time series represented through a set of basic trend TAs I = [Increasing] D = [Decreasing] S = [Steady] Definition of a set of significant patterns P = {p1, … , pN} c) Time series represented through complex TAs P1= [Increasing Decreasing] P2 =[Decreasing Increasing] APRIORI-like rule extraction algorithm OUTPUT: set of temporal rules Fig.

4 Diagnostic Task In order to do a diagnosis of these types of diseases we can now represent the report of a patient presumably affected by an exanthematic disorder in terms of temporal information about patient symptoms. By adding new information from the report into the networks built on the basis of standard disease data, the network consistency can be confirmed or it can be lost. The analysis of the changes of the consistency in the networks constitutes a first approach to diagnostic task for exanthematic diseases.

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