Artificial Intelligence in Medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005, ProceedingsSilvia Miksch, Jim Hunter, Elpida Keravnou The European Society for Arti'cial Intelligence in Medicine (AIME) was est- lishedin1986withtwomaingoals:1)tofosterfundamentalandappliedresearch in the application of Arti'cial Intelligence (AI) techniques to medical care and medical research, and 2) to providea forum at biennial conferences for reporting signi'cant results achieved. Additionally, AIME assists medical industrialists to identify newAItechniqueswithhighpotentialforintegrationintonewproducts. Amajoractivityofthissocietyhasbeenaseriesofinternationalconferencesheld biennially over the last 18 years: Marseilles, France (1987), London, UK (1989), Maastricht, Netherlands (1991), Munich, Germany (1993), Pavia, Italy (1995), Grenoble, France (1997), Aalborg, Denmark (1999), Cascais, Portugal (2001), Protaras, Cyprus (2003). The AIME conference provides a unique opportunity to present and improve the international state of the art of AI in medicine from both a research and an applications perspective. For this purpose, the AIME conference includes invited lectures, contributed papers, system demonstrations, a doctoral cons- tium, tutorials, and workshops. The present volume contains the proceedings of AIME 2005, the 10th conference on Arti'cial Intelligence in Medicine, held in Aberdeen, Scotland, July 23-27, 2005. In the AIME 2005 conference announcement, we encouraged authors to s- mit original contributions to the development of theory, techniques, and - plications of AI in medicine, including the evaluation of health care programs. Theoretical papers were to include presentation or analysis of the properties of novelAImethodologiespotentiallyusefultosolvingmedicalproblems.Technical papers were to describe the novelty of the proposed approach, its assumptions, bene'ts, and limitations compared with other alternative techniques. Appli- tion papers were to present su'cient information to allow the evaluation of the practical bene'ts of the proposed system or methodology. |
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Contents
A Way Out of the Medical Tower of Babel? | 3 |
Learning Rules with Complex Temporal Patterns in Biomedical Domains | 23 |
Probabilistic Abstraction of Multiple Longitudinal Electronic Medical | 43 |
Decision Support Systems | 56 |
Extending Temporal Databases to Deal with TelicAtelic Medical Data | 58 |
An Expert System for Atherosclerosis Risk Assessment | 78 |
A Rehabilitation Expert System for Poststroke Patients Douglas D Dankel II Marıa Osk Kristmundsdóttir | 94 |
A Collaborative Activities Representation for Building | 111 |
The Use of Verbal Classification in Determining the Course of Medical | 276 |
Interactive Knowledge Validation in CBR for Decision Support | 287 |
Adaptation and Medical CaseBased Reasoning Focusing on Endocrine | 300 |
Towards Information Visualization and Clustering Techniques for | 315 |
Automatic Landmarking of Cephalograms by Cellular Neural Networks | 333 |
Morphometry of the Hippocampus Based on a Deformable Model | 353 |
Multiagent Patient Representation in Primary Care | 375 |
Clinical Reasoning Learning with Simulated Patients | 385 |
Improving Clinical Guideline Implementation Through Prototypical | 126 |
Helping Physicians to Organize Guidelines Within Conceptual | 141 |
MHB A ManyHeaded Bridge Between Informal and Formal | 146 |
A HistoryBased Algebra for QualityChecking Medical Guidelines | 161 |
Gaining Process Information from Clinical Practice Guidelines Using | 181 |
Formalising Medical Quality Indicators to Improve Guidelines | 201 |
OntologyMediated Distributed Decision Support for Breast Cancer | 221 |
Building Medical Ontologies Based on Terminology Extraction from | 231 |
Using Lexical and Logical Methods for the Alignment of Medical | 241 |
A Benchmark Evaluation of the French MeSH Indexers | 251 |
Ontology of Time and Situoids in Medical Conceptual Modeling | 266 |
Implicit Learning System for Teaching the Art of Acute Cardiac | 395 |
Web Mining Techniques for Automatic Discovery of Medical Knowledge | 409 |
An Evolutionary Divide and Conquer Method for LongTerm Dietary | 419 |
Selecting Decision Support Algorithm for | 429 |
Subgroup Mining for Interactive Knowledge Refinement | 453 |
On Understanding and Assessing Feature Selection Bias | 468 |
Learning Rules from Multisource Data for Cardiac Monitoring | 484 |
Signature Recognition Methods for Identifying Influenza Sequences | 504 |
An Algorithm to Learn Causal Relations Between Genes from Steady | 524 |
Other editions - View all
Artificial Intelligence in Medicine Elpida Keravnou,Catherine Garbay,Robert Baud No preview available - 2014 |
Artificial Intelligence in Medicine: 10th Conference on Artificial ... Silvia Miksch,Jim Hunter,Elpida Keravnou No preview available - 2005 |
Common terms and phrases
Abstract accuracy actions activity adaptation AIME algorithm allows analysis application approach attributes automatically build classification clinical combination compared complex Computer concepts considered consists constraints contains corresponding decision defined described detection developed diagnosis disease distribution domain effective error evaluation example expert expression extraction Figure formal function gene given guideline identified important improve indicators instances Intelligence interaction Italy knowledge language learning means measure Medicine method mining observed obtained ontology patient patterns performance possible practice predictions present probability problem proposed reasoning References region relations relevant represent representation rules score selection semantic sequences shows similar specific step structure Table task techniques templates temporal tion tool treatment tree University validation values variables visualization