Artificial Intelligence in Medicine [electronic resource] : 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005. Proceedings / edited by Silvia Miksch, Jim Hunter, Elpida T. Keravnou.

By: Miksch, Silvia [editor.]Contributor(s): Hunter, Jim [editor.] | Keravnou, Elpida T [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 3581Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: XVII, 547 p. Also available online. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540318842Subject(s): Computer science | Medical records -- Data processing | Database management | Information storage and retrieval systems | Information systems | Artificial intelligence | Computer vision | Computer Science | Artificial Intelligence (incl. Robotics) | Health Informatics | Image Processing and Computer Vision | Information Systems Applications (incl.Internet) | Information Storage and Retrieval | Database ManagementAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access online
Contents:
Invited Talks -- Ontology Mapping: A Way Out of the Medical Tower of Babel? -- Human Computer Interaction in Context Aware Wearable Systems -- Temporal Representation and Reasoning -- A New Approach to the Abstraction of Monitoring Data in Intensive Care -- Learning Rules with Complex Temporal Patterns in Biomedical Domains -- Discriminating Exanthematic Diseases from Temporal Patterns of Patient Symptoms -- Probabilistic Abstraction of Multiple Longitudinal Electronic Medical Records -- Using a Bayesian-Network Model for the Analysis of Clinical Time-Series Data -- Data-Driven Analysis of Blood Glucose Management Effectiveness -- Extending Temporal Databases to Deal with Telic/Atelic Medical Data -- Dichotomization of ICU Length of Stay Based on Model Calibration -- Decision Support Systems -- AtherEx: An Expert System for Atherosclerosis Risk Assessment -- Smooth Integration of Decision Support into an Existing Electronic Patient Record -- REPS: A Rehabilitation Expert System for Post-stroke Patients -- Clinical Guidelines and Protocols -- Testing Asbru Guidelines and Protocols for Neonatal Intensive Care -- EORCA: A Collaborative Activities Representation for Building Guidelines from Field Observations -- Design Patterns for Modelling Guidelines -- Improving Clinical Guideline Implementation Through Prototypical Design Patterns -- Automatic Derivation of a Decision Tree to Represent Guideline-Based Therapeutic Strategies for the Management of Chronic Diseases -- Exploiting Decision Theory for Supporting Therapy Selection in Computerized Clinical Guidelines -- Helping Physicians to Organize Guidelines Within Conceptual Hierarchies -- MHB – A Many-Headed Bridge Between Informal and Formal Guideline Representations -- Clinical Guidelines Adaptation: Managing Authoring and Versioning Issues -- Open-Source Publishing of Medical Knowledge for Creation of Computer-Interpretable Guidelines -- A History-Based Algebra for Quality-Checking Medical Guidelines -- The Spock System: Developing a Runtime Application Engine for Hybrid-Asbru Guidelines -- AI Planning Technology as a Component of Computerised Clinical Practice Guidelines -- Gaining Process Information from Clinical Practice Guidelines Using Information Extraction -- Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical Guidelines -- Formalising Medical Quality Indicators to Improve Guidelines -- Ontology and Terminology -- Oncology Ontology in the NCI Thesaurus -- Ontology-Mediated Distributed Decision Support for Breast Cancer -- Multimedia Data Management to Assist Tissue Microarrays Design -- Building Medical Ontologies Based on Terminology Extraction from Texts: Methodological Propositions -- Translating Biomedical Terms by Inferring Transducers -- Using Lexical and Logical Methods for the Alignment of Medical Terminologies -- Latent Argumentative Pruning for Compact MEDLINE Indexing -- A Benchmark Evaluation of the French MeSH Indexers -- Populating an Allergens Ontology Using Natural Language Processing and Machine Learning Techniques -- Ontology of Time and Situoids in Medical Conceptual Modeling -- The Use of Verbal Classification for Determining the Course of Medical Treatment by Medicinal Herbs -- Case-Based Reasoning, Signal Interpretation, Visual Mining -- Interactive Knowledge Validation in CBR for Decision Support in Medicine -- Adaptation and Medical Case-Based Reasoning Focusing on Endocrine Therapy Support -- Transcranial Magnetic Stimulation (TMS) to Evaluate and Classify Mental Diseases Using Neural Networks -- Towards Information Visualization and Clustering Techniques for MRI Data Sets -- Computer Vision and Imaging -- Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps -- Automatic Landmarking of Cephalograms by Cellular Neural Networks -- Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure -- Morphometry of the Hippocampus Based on a Deformable Model and Support Vector Machines -- Automatic Segmentation of Whole-Body Bone Scintigrams as a Preprocessing Step for Computer Assisted Diagnostics -- Knowledge Management -- Multi-agent Patient Representation in Primary Care -- Clinical Reasoning Learning with Simulated Patients -- Implicit Learning System for Teaching the Art of Acute Cardiac Infarction Diagnosis -- Which Kind of Knowledge Is Suitable for Redesigning Hospital Logistic Processes? -- Machine Learning, Knowledge Discovery and Data Mining -- Web Mining Techniques for Automatic Discovery of Medical Knowledge -- Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies -- An Evolutionary Divide and Conquer Method for Long-Term Dietary Menu Planning -- Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series -- Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System -- A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining -- Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach -- Subgroup Mining for Interactive Knowledge Refinement -- Evidence Accumulation to Identify Discriminatory Signatures in Biomedical Spectra -- On Understanding and Assessing Feature Selection Bias -- A Model-Based Approach to Visualizing Classification Decisions for Patient Diagnosis -- Learning Rules from Multisource Data for Cardiac Monitoring -- Effective Confidence Region Prediction Using Probability Forecasters -- Signature Recognition Methods for Identifying Influenza Sequences -- Conquering the Curse of Dimensionality in Gene Expression Cancer Diagnosis: Tough Problem, Simple Models -- An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset -- Relation Mining over a Corpus of Scientific Literature.
In: Springer eBooksSummary: 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.
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Invited Talks -- Ontology Mapping: A Way Out of the Medical Tower of Babel? -- Human Computer Interaction in Context Aware Wearable Systems -- Temporal Representation and Reasoning -- A New Approach to the Abstraction of Monitoring Data in Intensive Care -- Learning Rules with Complex Temporal Patterns in Biomedical Domains -- Discriminating Exanthematic Diseases from Temporal Patterns of Patient Symptoms -- Probabilistic Abstraction of Multiple Longitudinal Electronic Medical Records -- Using a Bayesian-Network Model for the Analysis of Clinical Time-Series Data -- Data-Driven Analysis of Blood Glucose Management Effectiveness -- Extending Temporal Databases to Deal with Telic/Atelic Medical Data -- Dichotomization of ICU Length of Stay Based on Model Calibration -- Decision Support Systems -- AtherEx: An Expert System for Atherosclerosis Risk Assessment -- Smooth Integration of Decision Support into an Existing Electronic Patient Record -- REPS: A Rehabilitation Expert System for Post-stroke Patients -- Clinical Guidelines and Protocols -- Testing Asbru Guidelines and Protocols for Neonatal Intensive Care -- EORCA: A Collaborative Activities Representation for Building Guidelines from Field Observations -- Design Patterns for Modelling Guidelines -- Improving Clinical Guideline Implementation Through Prototypical Design Patterns -- Automatic Derivation of a Decision Tree to Represent Guideline-Based Therapeutic Strategies for the Management of Chronic Diseases -- Exploiting Decision Theory for Supporting Therapy Selection in Computerized Clinical Guidelines -- Helping Physicians to Organize Guidelines Within Conceptual Hierarchies -- MHB – A Many-Headed Bridge Between Informal and Formal Guideline Representations -- Clinical Guidelines Adaptation: Managing Authoring and Versioning Issues -- Open-Source Publishing of Medical Knowledge for Creation of Computer-Interpretable Guidelines -- A History-Based Algebra for Quality-Checking Medical Guidelines -- The Spock System: Developing a Runtime Application Engine for Hybrid-Asbru Guidelines -- AI Planning Technology as a Component of Computerised Clinical Practice Guidelines -- Gaining Process Information from Clinical Practice Guidelines Using Information Extraction -- Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical Guidelines -- Formalising Medical Quality Indicators to Improve Guidelines -- Ontology and Terminology -- Oncology Ontology in the NCI Thesaurus -- Ontology-Mediated Distributed Decision Support for Breast Cancer -- Multimedia Data Management to Assist Tissue Microarrays Design -- Building Medical Ontologies Based on Terminology Extraction from Texts: Methodological Propositions -- Translating Biomedical Terms by Inferring Transducers -- Using Lexical and Logical Methods for the Alignment of Medical Terminologies -- Latent Argumentative Pruning for Compact MEDLINE Indexing -- A Benchmark Evaluation of the French MeSH Indexers -- Populating an Allergens Ontology Using Natural Language Processing and Machine Learning Techniques -- Ontology of Time and Situoids in Medical Conceptual Modeling -- The Use of Verbal Classification for Determining the Course of Medical Treatment by Medicinal Herbs -- Case-Based Reasoning, Signal Interpretation, Visual Mining -- Interactive Knowledge Validation in CBR for Decision Support in Medicine -- Adaptation and Medical Case-Based Reasoning Focusing on Endocrine Therapy Support -- Transcranial Magnetic Stimulation (TMS) to Evaluate and Classify Mental Diseases Using Neural Networks -- Towards Information Visualization and Clustering Techniques for MRI Data Sets -- Computer Vision and Imaging -- Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps -- Automatic Landmarking of Cephalograms by Cellular Neural Networks -- Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure -- Morphometry of the Hippocampus Based on a Deformable Model and Support Vector Machines -- Automatic Segmentation of Whole-Body Bone Scintigrams as a Preprocessing Step for Computer Assisted Diagnostics -- Knowledge Management -- Multi-agent Patient Representation in Primary Care -- Clinical Reasoning Learning with Simulated Patients -- Implicit Learning System for Teaching the Art of Acute Cardiac Infarction Diagnosis -- Which Kind of Knowledge Is Suitable for Redesigning Hospital Logistic Processes? -- Machine Learning, Knowledge Discovery and Data Mining -- Web Mining Techniques for Automatic Discovery of Medical Knowledge -- Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies -- An Evolutionary Divide and Conquer Method for Long-Term Dietary Menu Planning -- Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series -- Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System -- A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining -- Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach -- Subgroup Mining for Interactive Knowledge Refinement -- Evidence Accumulation to Identify Discriminatory Signatures in Biomedical Spectra -- On Understanding and Assessing Feature Selection Bias -- A Model-Based Approach to Visualizing Classification Decisions for Patient Diagnosis -- Learning Rules from Multisource Data for Cardiac Monitoring -- Effective Confidence Region Prediction Using Probability Forecasters -- Signature Recognition Methods for Identifying Influenza Sequences -- Conquering the Curse of Dimensionality in Gene Expression Cancer Diagnosis: Tough Problem, Simple Models -- An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset -- Relation Mining over a Corpus of Scientific Literature.

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.

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