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 6-10 of 78
Page 16
... method uses a simple formalism for the symbolization of signal local variation, for writing words, and uses ways of visual display of processing results adapted to medical `Odéj`a-vu ́O. 3. The third element of answer is that the ...
... method uses a simple formalism for the symbolization of signal local variation, for writing words, and uses ways of visual display of processing results adapted to medical `Odéj`a-vu ́O. 3. The third element of answer is that the ...
Page 24
... method for the discovery of both association and temporal rules to get an insight into the possible causes of nonadherence to therapeutic protocols in hemodialysis, through the analysis of a set of monitoring variables. The TDM approach ...
... method for the discovery of both association and temporal rules to get an insight into the possible causes of nonadherence to therapeutic protocols in hemodialysis, through the analysis of a set of monitoring variables. The TDM approach ...
Page 25
... method is to provide interesting results on real data sets. Finally we discuss pros e cons of the proposed approach. 2. The. Complex. Temporal. Rules. Extraction. Method. As shown in Figure 1, the method proposed in this paper develops ...
... method is to provide interesting results on real data sets. Finally we discuss pros e cons of the proposed approach. 2. The. Complex. Temporal. Rules. Extraction. Method. As shown in Figure 1, the method proposed in this paper develops ...
Page 31
... method for the automated generation of temporal rules which involve complex patterns in both the antecedent and the consequent. This algorithm is particularly suited for exploring the temporal relationships between the variables ...
... method for the automated generation of temporal rules which involve complex patterns in both the antecedent and the consequent. This algorithm is particularly suited for exploring the temporal relationships between the variables ...
Page 43
... methods suggested for solving the TA task as well as closely related systems applied in the clinical domain (e.g., [5-7]). Thus, Considering these challenging subproblems suggests an additional method. At least three subproblems in the ...
... methods suggested for solving the TA task as well as closely related systems applied in the clinical domain (e.g., [5-7]). Thus, Considering these challenging subproblems suggests an additional method. At least three subproblems in the ...
Contents
3 | |
23 | |
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