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. |
From inside the book
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... Mining. Interactive Knowledge Validation in CBR for Decision Support in Medicine Monica Ou, Geoff A.W. West, Mihai Lazarescu, Chris Clay ......... 289 Adaptation and Medical Case-Based Reasoning, Focusing on Endocrine Therapy Support XIV ...
... Mining. Interactive Knowledge Validation in CBR for Decision Support in Medicine Monica Ou, Geoff A.W. West, Mihai Lazarescu, Chris Clay ......... 289 Adaptation and Medical Case-Based Reasoning, Focusing on Endocrine Therapy Support XIV ...
Page xvi
... Mining Techniques for Automatic Discovery of Medical Knowledge David Sánchez, Antonio Moreno ... Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System Jerzy Blaszczynski, Ken Farion, Wojtek Michalowski, Szymon ...
... Mining Techniques for Automatic Discovery of Medical Knowledge David Sánchez, Antonio Moreno ... Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System Jerzy Blaszczynski, Ken Farion, Wojtek Michalowski, Szymon ...
Page xvii
... Mining for Interactive Knowledge Refinement Martin Atzmueller, Joachim Baumeister, Achim Hemsing, Ernst-Jürgen Richter, Frank Puppe ............................... Evidence Accumulation for Identifying Discriminatory Signatures in ...
... Mining for Interactive Knowledge Refinement Martin Atzmueller, Joachim Baumeister, Achim Hemsing, Ernst-Jürgen Richter, Frank Puppe ............................... Evidence Accumulation for Identifying Discriminatory Signatures in ...
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 |
Extending Temporal Databases to Deal with TelicAtelic Medical Data | 58 |
An Expert System for Atherosclerosis Risk Assessment | 78 |
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 |
Automatic Landmarking of Cephalograms by Cellular Neural Networks | 333 |
Recognizing Explicit and Implicit | 343 |
Morphometry of the Hippocampus Based on a Deformable Model | 353 |
Automatic Segmentation of WholeBody Bone Scintigrams as | 363 |
Multiagent Patient Representation in Primary Care | 375 |
Clinical Reasoning Learning with Simulated Patients | 385 |
Which Kind of Knowledge Is Suitable for Redesigning Hospital Logistic | 400 |
Mining | 409 |
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 |
The Use of Verbal Classification in Determining the Course of Medical | 276 |
Adaptation and Medical CaseBased Reasoning Focusing on Endocrine | 300 |
Transcranial Magnetic Stimulation TMS to Evaluate and Classify | 310 |
Towards Automated Interpretation | 321 |
An Evolutionary Divide and Conquer Method for LongTerm Dietary | 419 |
Interactive Knowledge Validation in CBR for Decision Support | 423 |
A Data Preprocessing Method to Increase Efficiency and Accuracy | 434 |
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 |
| 545 | |
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 adaptation agent algorithm allergen analysis annotation application approach Artificial Intelligence Asbru automatically Bayesian Bayesian network Berlin Heidelberg 2005 biomedical cancer Case-Based Reasoning classification clinical guidelines complex Computer concepts constraints data mining database dataset decision support decision tree defined described detection developed diagnosis disease domain evaluation example expert extraction feature FiO2 formal function fuzzy gene graph hippocampus identify implemented influence diagram interaction keratoconus knowledge base landmarks LNAI machine learning mapping Medical Informatics Medicine method Miksch multi-agent systems multisource n-gram neural network node obtained ontology parameters patient patterns performed points problem Proc query relations relevant represent representation retrieval rules scintigraphy selection semantic sequences sketch spatial specific Springer-Verlag Berlin Heidelberg step structure student subgroup Support Vector Machines task TeachMed techniques templates temporal therapy tion treatment values variables vector visualization