General Information

[Introduction] [Scope of Conference] [Invited Speakers]

Introduction

The European Societies for Artificial Intelligence in Medicine (AIME) and Medical Decision Making (ESMDM) were both established in 1986. The aims of AIME and ESMDM are the following:

AIME

• to foster fundamental and applied research in the application of Artificial Intelligence (AI) techniques to medical care and medical research, and
  
• to provide a forum for reporting significant results achieved, at biennial conferences.

ESMDM

• to promote research and training in medical decision-making, and
  
• to provide a forum for circulating ideas and programs of related interest

A major activity of both these societies has been a series of international conferences, held biennially over the last 12 years.

Scope of Conference

For the first time AIMDM´99 will see a joint conference of the European societies for Artificial Intelligence in Medicine (AIME) and Medical Decision Making (ESMDM), building on common interests in the representation and application of medical knowledge in medical decision making.

The societies seek to publish original contributions to the development of theory, techniques, and applications of both AI in Medicine and medical decision making (MDM). Contributions to theory may include presentation or analysis of the properties of novel AI or MDM methodologies potentially useful to solve medical problems. Papers on techniques should describe the development or the extension of AI or MDM methods and their implementation. They should also discuss the assumptions and limitations which characterize the proposed methods. Application papers should describe the implementation of AI or MDM systems to solve significant medical problems, including health care quality assurance, health care costs and ethical considerations. Application papers should present sufficient information to allow evaluation of the practical benefits of using the system.

AIMDM'99 will provide:

• Invited papers
• Contributed papers, abstracts and posters
• System demonstrations
• Tutorials to introduce newcomers and discuss advanced topics
• Workshops

The scope includes the following methodological areas:

AI in Medicine

• Knowledge acquisition, representation, refinement, validation and maintenance
• Machine learning and data mining
• Decision support systems, including knowledge based systems, neural networks, belief networks and statistical models
• Uncertain, temporal, and case based reasoning
• Planning and scheduling
• Natural language generation and understanding
• Computer vision, image and signal interpretation
• Intelligent agents and information retrieval
• Telemedicine and knowledge management in intranets and the internet
• Cognitive modelling

Medical Decision Making

• Strategies, guidelines and applications dealing with diagnosis, treatment and follow-up of patients
• Health care quality assurance, improvement and clinical audit
• Health economics, cost effectiveness, resource allocation
• Ethical, regulatory, legal and psychological aspects of medical decision making and decision support tools
• Technology assessment and health policy making
• Decision analysis, utility assessment and decision modeling
• Evidence based medicine and clinical effectiveness

Invited Speakers

In addition to the above activities AIMDM´99 includes lectures from eminent speakers in the fields of Artificial Intelligence and decision making in medicine. Preliminary titles for these lectures are:

• Artificial Intelligence for Learning Health Care Organizations.
  Prof. Mario Stefanelli
  Laboratory of Medical Informatics, Department of Computers and System Science.
  University of Pavia, Italy.
• From Clinical Guideliness to Decision Support.
  Prof. Gianpaolo Molino
  Department of Internal Medicine.
  Azienda Ospedaliera San Giovanni Battista di Torino, Italy.
• Machine Learning for Data Mining in Medicine.
  Prof. Nada Lavrac
  Department of Intelligent Systems.
  J. Stefan Institute, Lubljana, Slovenia.
• Timing is Everything: Temporal Reasoning and Temporal Data Maintenance in Medicine.
  Prof. Yuval Shahar
  Medical Informatics.
  Stanford University School of Medicine, California, USA.

[ Back ]