By Silvia Miksch, Jim Hunter, Elpida Keravnou
This publication constitutes the refereed complaints 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 awarded including 2 invited contributions have been rigorously reviewed and chosen from 148 submissions. The papers are prepared in topical sections on temporal illustration and reasoning, selection help structures, medical instructions and protocols, ontology and terminology, case-based reasoning, sign interpretation, visible mining, machine imaginative and prescient and imaging, wisdom administration, laptop studying, wisdom discovery, and information mining.
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Extra info for Artificial intelligence in medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005; proceedings
Statistical pattern detection in univariate time series of intensive care online monitoring data. Intensive Care Medicine 24 (1998) 1305–1314 13. : the median filter as a preprocessor for a patient monitor limit alarm system in intensive care. Computer Methods and Programs in Biomedicine 34 (1991) 139–144 14. : towards symbolization using data-driven extraction of local trends for icu monitoring. Artiﬁcial Intell. Med. 1-2 (2000) 203–223 15. : learning qualitative models of dynamics systems. Machine Learning 26 (1997) 177–211 16.
The building process then traces back the detected sequence and replaces its constituents in the new chain (m1 → m2) by the conclusion of the recognized chain (Seq1). Advantages of the system are the simultaneous detection of learned sequences and their use in building the currently learned sequence, and the mixing of abstraction levels. The system could learn sequences with the associated actions, if any. The information provided to the clinician could then be to propose some actions according to the current sequence.
This model accounts also for uncertainty and for causal links. Again, our work is only Discriminating Exanthematic Diseases from Temporal Patterns 41 devoted to deal with temporal aspects of the domain. Moreover the problem of integration of diﬀerent types of constraints is tackled in our work in the line of Meiri’s classical approach . In this paper we have shown an application of our temporal constraint solver in a medical domain; this application could support the physician to make a diagnosis of the exanthematic diseases on the basis of their temporal patterns.