AIME 91

This proceedings volume contains 26 papers describing the state of the art in artificial intelligence in medicine research in Europe. Besides the description of clinical appli- cations, it contains reports on fundamental research issues such as knowledge acquisition and representation, modelling and uncertainty management.

Contenu
Keynote Addresses.- Model-based Image Segmentation: Methods and Applications.- Real versus Artificial Expertise: The Development of Cognitive Models of Clinical Reasoning.- Methodology.- A Developmental Perspective on the Role of Biomedical Knowledge in Medical Problem Solving; Implications for Al.- Reconstructing Medical Problem Solving Competence: MACCORD.- The Role of Domain Models in Maintaining Consistency of Large Medical Knowledge Bases.- Knowledge Representation.- A Framework for Causal Reasoning with a Functional Approach.- Modelling and Knowledge (Re)presentation within HIOS+.- Medical Knowledge Representation and Predictive Data Entry.- Clinical Applications.- A Connectionist Aid to the Early Diagnosis of Myocardial Infarction.- Automation of Medical Audit in General Practice.- An Intelligent System for Monitoring Infections in Heart Transplant Recipients.- Measuring Performance of a Bayesian Decision Support System for the Diagnosis of Rheumatic Disorders.- The Application of Distributed Artificial Intelligence to Medical Diagnosis.- Knowledge Representation of Discharge Summaries.- Modelling.- Dual Teleological Perspectives in Qualitative Circulatory Analysis.- Physiological Modelling Using RL.- Integrated Use of Causal and Algebraic Physiological Models to Support Anaesthetists in Decision Making.- Uncertainty Management.- Cytotoxic Chemotherapy Monitoring Using Stochastic Simulation on Graphical Models.- A Model-Based Approach to Insulin Adjustment.- A Blackboard Control Architecture for Therapy Planning.- Knowledge Acquisition.- A Comparative Evaluation of Three Approaches to the Acquisition of Medical Knowledge.- A Knowledge Acquisition Tool for Medical Diagnostic Knowledge-Based Systems.- Machine Learning in Data Rich Domains: Some Experiences from theKAVAS Project.- The User Perspective.- Patient's and Physician's Opinion about Computer Expert Systems.- Designing an Adaptive Interface for EPIAIM.- CAP: A Critiquing Expert System for Medical Education.