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 This book constitutes the refereed proceedings of the 10th Conference on Artificial Intelligence in Medicine in Europe, AIME 2005, held in Aberdeen, UK in July 2005. The 35 revised full papers and 34 revised short papers presented together with 2 invited contributions were carefully reviewed and selected from 148 submissions. The papers are organized in topical sections on temporal representation and reasoning, decision support systems, clinical guidelines and protocols, ontology and terminology, case-based reasoning, signal interpretation, visual mining, computer vision and imaging, knowledge management, machine learning, knowledge discovery, and data mining. |
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Results 1-5 of 85
Page xvii
... Problem, Simple Models Minca Mramor, Gregor Leban, Janez Demšar, Blaz Zupan .......... 514 An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset Xin Zhang, Chitta ...
... Problem, Simple Models Minca Mramor, Gregor Leban, Janez Demšar, Blaz Zupan .......... 514 An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset Xin Zhang, Chitta ...
Page 3
... Problem Integration of different information sources has been a problem that has been challenging (or perhaps better: plaguing) Computer Science throughout the decades. As soon as we had two computers, we wanted to exchange information ...
... Problem Integration of different information sources has been a problem that has been challenging (or perhaps better: plaguing) Computer Science throughout the decades. As soon as we had two computers, we wanted to exchange information ...
Page 4
... problem was defined on simple strings that were names of record-fields; the schema-integration problem already had the full relational model as input; while ontology mapping problems are defined on full hierarchical models plus rich ...
... problem was defined on simple strings that were names of record-fields; the schema-integration problem already had the full relational model as input; while ontology mapping problems are defined on full hierarchical models plus rich ...
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... problem of semantic integration is one of the key problems facing Computer Science today. Despite many years of work, this old problem is still open, and has actually acquired a new urgency now that other integration barriers (physical ...
... problem of semantic integration is one of the key problems facing Computer Science today. Despite many years of work, this old problem is still open, and has actually acquired a new urgency now that other integration barriers (physical ...
Page 6
... problem for gene symbols. Technical report, Erasmus University Medical Center Rotterdam, 2004. Report D4.4 for the ... problems. Technical report, Statistical Research Division, U.S. Bureau of the Census, Washington, DC, 1999. Human ...
... problem for gene symbols. Technical report, Erasmus University Medical Center Rotterdam, 2004. Report D4.4 for the ... problems. Technical report, Statistical Research Division, U.S. Bureau of the Census, Washington, DC, 1999. Human ...
Contents
3 | |
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 | 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 |
Which Kind of Knowledge Is Suitable for Redesigning Hospital Logistic | 400 |
An Evolutionary Divide and Conquer Method for LongTerm Dietary | 419 |
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 |
Author Index | 545 |
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Common terms and phrases
Abstract accuracy actions AIME algorithm allergen analysis annotation application approach Artificial Intelligence Asbru automatically Bayesian network Berlin Heidelberg 2005 biomedical cancer Case-Based Reasoning classification clinical guidelines complex Computer concepts constraints corpus CPGs data mining database dataset decision support decision tree defined described detection developed diabetes diagnosis disease domain evaluation example expert extraction feature FiO2 formal function gene graph Heidelberg identified implemented indicators interaction keratoconus knowledge base language machine learning Medical Informatics Medicine method Miksch multi-agent systems multisource n-gram neural network node obtained ontology paper parameters patient patterns performance problem Proc proposed query region predictions relations relevant represent representation rules score selection semantic sequences sketch specific Springer-Verlag Berlin Heidelberg step structure subgroup Support Vector Machines task TeachMed techniques templates temporal therapy threshold tion topological ordering treatment values variables visualization