AIMDM'99 -- Call for Participation

Workshop: Prognostic Models in Medicine --
Artificial Intelligence and Decision Analytic Approaches

during the
Joint European Conference on Artificial Intelligence
in Medicine and Medical Decision Making (AIMDM'99)
in Aalborg, Denmark, 20th - 24th June 1999

(WWW version of this CFP:

Important dates


Prognostic models are increasingly used in medicine to predict the natural course of disease, or the expected outcome after treatment. Prognosis forms an integral part of systems for treatment selection and treatment planning. In evaluating quality of care, prognostic models are used for predicting outcome, such as mortality, which is compared with the actual measured outcome. Furthermore, prognostic models may play an important role in guiding diagnostic problem solving, e.g. by only requesting information concerning tests, of which the outcome affects knowledge of the prognosis. (See the following introductory paper on prognosis, which was presented during CESA'98.)

In recent years several methods and techniques from the fields of artificial intelligence, decision theory and statistics have been introduced into models of the medical management of patients (diagnosis, treatment, follow-up); in some of these models, assessment of the expected prognosis constitutes an integral part. Typically, recent prognostic methods rely on explicit (patho)physiological models, which may be combined with traditional models of life expectancy. Examples of such domain models are causal disease models, and physiological models of regulatory mechanisms in the human body. Such model-based approaches have the potential to facilitate the development of actual systems, because the medical domain models can be (partially) obtained from the medical literature.

Various methods have been suggested for the representations of such domain models ranging from quantitative and probabilistic approaches to symbolic and qualitative ones. Semantic concepts such as time, e.g. for modelling the progressive changes of regulatory mechanisms, have formed an important and challenging modelling issue. Moreover, automatic learning techniques of such models have been proposed. When model construction is hard, less explicit domain models have been studied such as the use of case-based and neural network representations and their combination with more explicit domain models. In medical decision analysis, where the theories of probability and utility are combined, various representations and techniques are suggested such as decision trees, regression models, and representations in which advantage is taken from the Markov assumption (such as in Markov decision problems).

This workshop aims at bringing together various theoretical and practical approaches to computational prognosis that comprise the state of the art in this field. This workshop is a follow up on the initiative started with the successful invited session on "Intelligent Prognostic Methods in Medical Diagnosis and Treatment Planning" in 1998 during the conference "Computational Engineering in Systems Applications 1998" (CESA'98) which has resulted in a special issue on prognosis of the journal Artificial Intelligence in Medicine.

Papers are sought that describe medical prognosis applications using methods and techniques from artificial intelligence, decision theory, and statistics as well as papers proposing theoretical foundations of such methods. The workshop will also include one or more invited talks (details will appear in due time on the corresponding WWW-page of this workshop and the AIMDM'99 pages).

Topics of interest

Submissions have been refereed by at least two members of the programme committee. Accepted papers will appear in the working notes of the workshop "Prognostic Models in Medicine: Artificial Intelligence and Decision Analytic Approaches". Authors of the best papers are invited to contribute to a special issue on prognostic models in medicine of the international journal Methods of Information in Medicine.

Instructions to authors

The final versions of the papers (up to 5 pages) are to be sent as a Postscript file by e-mail before 29 May 1999 to both co-chairs and should be written in English with a brief abstract. Formatting instructions are as follows: the abstract should be formatted in two-column format, with Times Roman type face, pointsize 10, with title and names of the authors in bold font. Left and right margins should be 2 cm, text height 23 cm, and text width 16.9 cm; the two columns should be separated by 0.5 cm white space. A sample paper is available: Sample paper.

Preferably the papers are produced using either LaTeX or MS-Word; there is a style file available for each of these text processing systems:

Registration fee

Workshop only 750 DKK, for participants of AIMDM'99: 500 DKK. The fee includes light refreshments and lunch.

Preliminary programme

Morning session

Chair: Ameen Abu-Hanna

12.25 - 14.00 Lunch

Afternoon session

Chair: Peter Lucas

Workshop organization

Ameen Abu-Hanna, University of Amsterdam, The Netherlands
Peter Lucas, Utrecht University, The Netherlands

Programme committee

A. Abu-Hanna, The Netherlands
S.S. Anand, UK
S. Andreassen, Denmark
P.M.M. Bossuyt, The Netherlands
J. Fox, UK
L.C. van der Gaag, The Netherlands
J.D.F. Habbema, The Netherlands
P. Haddawy, USA
P. Hammond, UK
E. Keravnou, Cyprus
N. Lavrac, Slovenia
J. van der Lei, The Netherlands
P.J.F. Lucas, The Netherlands
L. Ohno-Machado, USA
M. Ramoni, UK
M. Stefanelli, Italy
Th. Wetter, Germany
J. Wyatt, UK

For more information about the workshop please contact one of the co-chairs.

Ameen Abu-Hanna
Dept. of Medical Informatics 
Academic Medical Center 
University of Amsterdam 
Meibergdreef 15 
1105 AZ Amsterdam 
The Netherlands 
Telephone: +31 20 5664511 
Fax : +31 20 6912432
Peter Lucas
Dept. of Computer Science 
Utrecht University 
Padualaan 14 
3584 CH Utrecht 
The Netherlands 

Telephone: +31 30 2534094 
Fax: +31 30 2513791