By Silvia Miksch, Jim Hunter, Elpida Keravnou
This booklet constitutes the refereed lawsuits 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 awarded 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 structures, scientific instructions and protocols, ontology and terminology, case-based reasoning, sign interpretation, visible mining, computing device imaginative and prescient and imaging, wisdom administration, desktop studying, wisdom discovery, and information mining.
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Additional info for Artificial intelligence in medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005; proceedings
More detailed description of our integrated framework can be found in . 2 37 Algorithms The notions of local consistency have been extended too [6, 5]. In particular, local consistency has been expressed as the degree of satisfaction which denotes the acceptability of an assignment with respect to the soft constraints involved in the relative sub-network. According to , this degree of satisfaction corresponds to the least satisﬁed constraint. Moreover, Path-Consistency and Branch & Bound algorithms have been generalized to the fuzzy case adding some relevant reﬁnements that improve their eﬃciency.
Then the diagnosis can be based on the recognition of typical temporal structures. This is the case of exanthematic diseases. This paper deals with representation and reasoning on information concerning the evolution of physical parameters by means of a model based on Fuzzy Temporal Constraint Networks [12, 14]. Temporal information coming from the domain may be both qualitative such as “the interval I1 with fever precedes the interval I2 with skin rush” or metric such as “fever lasts one day” or mixed such as “symptom m2 follows symptom m1 and starts at 8pm”.
Therefore, the diagnosis of this kind of diseases can be based on the recognition of the typical temporal progression and duration of diﬀerent symptoms. To this aim, we propose to apply a temporal reasoning system we have developed. The system is able to handle both qualitative and metric temporal knowledge aﬀected by vagueness and uncertainty. 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 .