• Submission of final papers: 29 May 1999
• Conference: 20th - 24th June 1999
• Workshop: Sunday, 20th June 1999
• Invitation for authors of best papers to submit an extended version of the paper to the special issue of the international journal Methods of Information in Medicine:  June 1999

Scope

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

• Modelling and Reasoning:
• the specification of prognostic models, possibly as part of diagnostic or therapy-planning applications
• representation and reasoning about (multiple) model types such as empirical, anatomical and (patho)physiological ones
• representation of and reasoning with time
• qualitative representation and reasoning
• decision modelling and analysis
• (dynamic) probabilistic networks
• function-based representation and reasoning
• case-based representation and reasoning
• Knowledge Acquisition:
• acquisition of the medical prognostic models
• automated learning of domain or task models using machine learning and data-mining techniques
• Formalisation:
• use of logical, set-theoretical or probabilistic methods to formalise various aspects of prognosis and therapy planning
• Medical Applications:
• clinical context of actual prognostic models
• role of prognostic models in diagnosis or treatment planning of a specific disease
• evaluation of prognostic models

Instructions to authors

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

• The LaTeX style file should be included as follows:

\documenclass[two-columns]{article}

\usepackage{aimdm-ws}

• The MS-Word style file needs to be slightly changed according to the specifications given above.

Registration fee

Preliminary programme

Morning session

• 09.00 - 09.15 Coffee
• 09.15 - 09.30 Ameen Abu-Hanna and Peter Lucas: Introduction
• 09.30 - 10.15 J. Wyatt: invited talk - prognostic models in medicine
• 10.15 - 10.45 J.D.F. Habbema: Building prognostic models - statistical aspects
• 10.45 - 11.15 S.S. Anand, P.W. Hamilton, J.G. Hughes and D.A. Bell: Utilising censored neighbours in prognostication.
• 11.15 - 11.35 Coffee break
• 11.35 - 11.55 I. Zelic, N. Lavrac, P. Najdenov, Z. Rener-Primec: Impact of machine learning to the diagnosis and prognosis of first cerebral paroxysm.
• 11.55 - 12.25 R. Schmidt, B. Pollwein and L. Gierl: Prognoses for multiparametric time course of the kidney function.

12.25 - 14.00 Lunch

Afternoon session

• 14.00 - 14.45 K.G. Olesen: invited talk - medical models and Bayesian networks
• 14.45 - 15.15 H. Dreau, I. Colombet, P. Degoulet, G. Chatellieri: Identification of patients at high cardiovascular risk using a critical appraisal of statistical risk prediction models.
• 15.15 - 15.35 N. Peek: A specialised POMDP form and algorithm for clinical patient management.
• 15.35 - 15.55 Coffee break
• 15.55 - 16.25 M. Ramoni, P. Sebastian and R. Dybowski: Robust outcome prediction for intensive-care patients.
• 16.25 - 16.45 S. Antel, L.M. Li, F. Cendes, Z. Caramanos, A. Olivier, F. Andermann, F. Dubeau, R.E. Kearney, R. Shinghai, D.L. Arnold: A naive Bayesian classifier for the prediction of surgical outcome in patients with temporal lobe epilepsy.
• 16.45 - 17.00 Ameen Abu-Hanna and Peter Lucas: Conclusion

Workshop organization

Programme committee

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  A.Abu-Hanna@amc.uva.nl

Peter Lucas Dept. of Computer Science  Utrecht University  Padualaan 14  3584 CH Utrecht  The Netherlands  Telephone: +31 30 2534094  Fax: +31 30 2513791  lucas@cs.uu.nl