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. |
From inside the book
Results 1-5 of 70
Page 15
... values to belong to one or the other of fuzzy trends. The result is rendered in graphic form, overwriting in colors on each signal's trace the likelihood and the value of the trend detected by the system. Sentinel is a monitoring tool ...
... values to belong to one or the other of fuzzy trends. The result is rendered in graphic form, overwriting in colors on each signal's trace the likelihood and the value of the trend detected by the system. Sentinel is a monitoring tool ...
Page 16
... values to qualify signals. 4. Last, the level of temporal granularity chosen must allow to report both slow and fast components of each signal. We therefore analyze each signal on two separate time scales, the size of which is ...
... values to qualify signals. 4. Last, the level of temporal granularity chosen must allow to report both slow and fast components of each signal. We therefore analyze each signal on two separate time scales, the size of which is ...
Page 17
... value of the symbol calculated by the system, but is presented on another graph for more legibility in black and white (Figure 2). 1 For a set of values x of average μ and standard deviation s, the value of K is obtained from: K(x) = x ...
... value of the symbol calculated by the system, but is presented on another graph for more legibility in black and white (Figure 2). 1 For a set of values x of average μ and standard deviation s, the value of K is obtained from: K(x) = x ...
Page 18
... values of the gaussianity indexes G previously defined in section 2.1. PCA is computed in a time frame of w points, displaced gradually on the whole duration of the signals. The average value μ of the projected values on the first ...
... values of the gaussianity indexes G previously defined in section 2.1. PCA is computed in a time frame of w points, displaced gradually on the whole duration of the signals. The average value μ of the projected values on the first ...
Page 20
... value of the symbols corresponding to ABPS, ABPD, ABPM, SpO2, hRate,Vt, VE and rRate. Paw,max has not be included in the analysis. For more legibility, the (C)onstant values of the symbols are not represented 3 Perspectives Instead of ...
... value of the symbols corresponding to ABPS, ABPD, ABPM, SpO2, hRate,Vt, VE and rRate. Paw,max has not be included in the analysis. For more legibility, the (C)onstant values of the symbols are not represented 3 Perspectives Instead of ...
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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Artificial Intelligence in Medicine: 10th Conference on Artificial ... Silvia Miksch,Jim Hunter,Elpida Keravnou No preview available - 2005 |
Artificial Intelligence in Medicine: 10th Conference on Artificial ... Silvia Miksch,Jim Hunter,Elpida Keravnou No preview available - 2009 |
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