By Silvia Miksch, Jim Hunter, Elpida Keravnou
This publication constitutes the refereed court cases of the tenth convention on synthetic 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 geared up in topical sections on temporal illustration and reasoning, determination aid platforms, scientific instructions and protocols, ontology and terminology, case-based reasoning, sign interpretation, visible mining, desktop imaginative and prescient and imaging, wisdom administration, desktop studying, wisdom discovery, and knowledge 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
1996) 957–958 22 S. Sharshar, L. -C. Chambrin 4. : utilizing temporal data abstraction for data validation and therapy planning for artiﬁcially ventilated newborn infants. Artiﬁcial Intell. Med. 8 (1996) 543–576 5. : knowledge-based temporal abstraction in clinical domains. Artiﬁcial Intell. Med. 8 (1996) 267–298 6. : neoganesh : a working system for the automated control of assisted ventilation in icus. Artiﬁcial Intell. Med. 11 (1997) 97–117 7. : managing temporal worlds for medical trend diagnosis.
Artiﬁcial Intelligence 87 (1996) 343–385 19. : Learning and solving soft temporal constraints: An experimental study. In: CP 2002, Ithaca, NY, Springer Verlag (2002) 20. : Processing temporal constraint networks. Artiﬁcial Intelligence 93 (1995) 29–61 21. : On the use and usefulness of fuzzy sets in medical AI. Artiﬁcial Intelligence in Medicine 21 (2001) 131–137 22. : Fuzzy temporal/categorical information in diagnosis. B. il Abstract. Several systems have been designed to reason about longitudinal patient data in terms of abstract, clinically meaningful concepts derived from raw time-stamped clinical data.
TA mechanisms represent the fundamental step of TA-based analysis. Within TAs, we can distinguish between two main categories: basic and complex abstractions. Basic TAs are used to detect simple patterns in numerical or symbolic time series. More precisely, we can extract Trend Temporal Abstractions, to capture increasing, decreasing or stationary courses in a numerical time series, and State TAs, to detect qualitative patterns corresponding to low, high or normal values in a numerical or symbolic time series.