Artificial intelligence in medicine: 10th Conference on by Silvia Miksch, Jim Hunter, Elpida Keravnou PDF

By Silvia Miksch, Jim Hunter, Elpida Keravnou

ISBN-10: 3540278311

ISBN-13: 9783540278313

This booklet constitutes the refereed court cases of the tenth convention on synthetic Intelligence in drugs in Europe, AIME 2005, held in Aberdeen, united kingdom in July 2005.

The 35 revised complete papers and 34 revised brief papers provided including 2 invited contributions have been conscientiously reviewed and chosen from 148 submissions. The papers are prepared in topical sections on temporal illustration and reasoning, determination help platforms, medical guidance and protocols, ontology and terminology, case-based reasoning, sign interpretation, visible mining, machine imaginative and prescient and imaging, wisdom administration, computing device studying, wisdom discovery, and knowledge mining.

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Extra resources for Artificial intelligence in medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005; proceedings

Example text

G. ) thus making possible to find matches with data coming from the patient disease. 1 Introduction The necessity for the recruitment of symbolic approach to solve medical problems arises from the understanding that medical problems are often too complex to be modeled analytically [21]. An important contribution to symbolic approach is given by fuzzy methodologies that can be regarded as a formalism particularly suitable to deal with the imprecision intrinsic to many medical problems and useful to build models for biological systems when a precise model doesn’t exist or it is difficult to realize [16].

Temporal reasoning for decision support in medicine. Artif Intell Med. 33 (2005) 1-24 4. : A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14 (2002) 750-767 5. : An overview of temporal data mining. , Hegland, M. ): Proceedings of the 1st Australian Data Mining Workshop (ADM02), (2002) 83-90 6. : Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95 (1998)14863-14868 32 L. Sacchi et al. 7. : Bayesian clustering by dynamics.

The resulting representation is a label made up of simple qualitative elements of the kind [Increasing], [Decreasing], [Steady] (Figure 1 a) and b) ). 2. The Basic TA description represents the basis for the creation of the final complex TA representation, that will be then used in the rule extraction algorithm. 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.

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Artificial intelligence in medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005; proceedings by Silvia Miksch, Jim Hunter, Elpida Keravnou


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