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 80
Page 8
... action suggestions might also be provided. Finally the system might au- tomatically record data from instruments and the paramedics actions and send them to the hospital to help prepare for the patients arrival. Other health based ...
... action suggestions might also be provided. Finally the system might au- tomatically record data from instruments and the paramedics actions and send them to the hospital to help prepare for the patients arrival. Other health based ...
Page 9
... actions such as displaying a particular instructions or annotating a video recording is defined in the application. The aim of the context recognition process is to map a signal stream into this set of states. Thus context recognition ...
... actions such as displaying a particular instructions or annotating a video recording is defined in the application. The aim of the context recognition process is to map a signal stream into this set of states. Thus context recognition ...
Page 20
... actions that can be acquired automatically from the biomedical equipment (change in ventilatory therapy, drug therapy, alarm threshold, ...). The sequence output by the W.L.T.A process describes clinical events or situations depending ...
... actions that can be acquired automatically from the biomedical equipment (change in ventilatory therapy, drug therapy, alarm threshold, ...). The sequence output by the W.L.T.A process describes clinical events or situations depending ...
Page 21
... actions, if any. The information provided to the clinician could then be to propose some actions according to the current sequence. References 1. Coll: improving control of patient status in critical care: the improve project. Computer ...
... actions, if any. The information provided to the clinician could then be to propose some actions according to the current sequence. References 1. Coll: improving control of patient status in critical care: the improve project. Computer ...
Page 24
... actions in muscles [9] and the cell cycle in normal and tumor cells [10]. Several works have been proposed also for what concerns the representation and processing of time series coming from the monitoring of clinical parameters ...
... actions in muscles [9] and the cell cycle in normal and tumor cells [10]. Several works have been proposed also for what concerns the representation and processing of time series coming from the monitoring of clinical parameters ...
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 - 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