Bayesian Models in Medicine

Scope

Bayesian networks (also known as causal probabilistic networks) with their associated methods have now been around in medicine for more than a decade. They have become increasingly popular for representing and handling uncertain knowledge in medicine. Almost simultaneously, the use of Bayesian statistics in medicine has increased in popularity. Moreover, interest in Bayesian methods is emerging within bioinformatics, e.g. for building models for protein structure prediction and the interpretation of microarray gene expression data. This special issue aims to give an impression of the current state of the art of the use of Bayesian methods in these research fields.

The Bayesian approach has the advantage that it provides the machinary for incorporating evidence into statistical reasoning. Bayesian models are used in medicine to assist in the diagnosis of disorders and to predict the natural course of disease or outcome after treatment (prognosis). They are also being used as part of models to determine the optimal treatments of a disorder in patients, or to predict outcome in groups of patients. Furthermore, Bayesian model development is not only confined to extracting probabilistic information from datasets; graphical models like Bayesian networks can also be constructed with the help of biomedical domain experts or by consulting relevant biomedical literature. Typically, Bayesian networks rely for their construction on causal, in particular (patho)physiological models of disease. Bayesian networks have also been used successfully for the construction of dynamic, temporal statistical models. The fact that Bayesian models allow for the easy incorporation of knowledge of background populations, explains that they are also increasingly used in research on risk models of disease, associating risk with spatial distribution of populations. In the context of medical decision making, Bayesian models can be easily integrated with decision theory to yield models for the selection of optimal treatments, or to develop models for health-care planning under uncertainty.

Topics

It is expected that papers will explicitly discuss one or more of the topics mentioned below in the context of medicine.

Modelling and Reasoning

• Construction of Bayesian network models in collaboration with medical domain experts, using qualitative knowledge or decision-theoretic principles
• Bayesian modelling of biological mechanisms
• Specification languages for Bayesian models, such as probabilistic logic and graph-like languages
• Problem solving in medicine with Bayesian networks
• Computational issues, such as space and time complexity of algorithms, in reasoning with realistic medical Bayesian models
• Representation and manipulation of time in Bayesian networks
• Representation of spatial knowledge in Bayesian models
• Markov processes (MPs, MDPs and POMDPs)
• The notion of causality in medicine and human biology, and its representation in Bayesian networks
• Sensitivity analysis of Bayesian models in medicine
• Modelling of medical and human biology domain characteristics by means of Bayesian networks

Learning

• Problems of using real-world biomedical datasets for learning Bayesian models from data
• Using background knowledge, e.g. causal knowledge or knowledge about structure, to enhance learning Bayesian models from data
• Learning Bayesian network topologies from data
• Probability assessment in the context of the construction of Bayesian models from data
• Refinement of Bayesian models using data
• Handling missing values in data

Evaluation

• Methodologies for the evaluation of Bayesian models
• Evaluation of Bayesian networks for concrete medical domains

Submissions will be refereed by at least two and in most cases three members of the programme committee. Accepted papers will appear in the special issue of the journal Artificial Intelligence in Medicine on Bayesian Models in Medicine.

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Instructions to authors

The papers (up to 20 pages) are to be submitted as a compressed Postscript file by e-mail before 1st June 2002 to all three special-issue editors and should be written in English with a brief abstract and 5 to 7 keywords.

Preferably the papers are produced using LaTeX.

Special-issue editors

Peter Lucas
Linda van der Gaag
Ameen Abu-Hanna

Editorial board

A. Abu-Hanna, The Netherlands
K.-P. Adlassnig, Austria
R. Bellazzi, Italy
C. Berzuini, Italy
G.F. Cooper, USA
R.G. Cowell, UK
F.J. Diez, Spain
M.J. Druzdzel, USA
L.C. van der Gaag, The Netherlands
P. Haddawy, USA
D. Hand, UK
I.S. Kohane, USA
P. Larranaga, Spain
A. Lawson, UK
L. Leibovici, Israel
T.Y. Leong, Singapore
P.J.F. Lucas, The Netherlands
S. Monti, USA
L. Ohno-Machado, USA
K.G. Olesen, Denmark
M. Paul, Israel
M. Ramoni, USA
A. Riva, USA
P. Sebastiani, USA
G. Tusch, Germany
J. Wyatt, UK
B. Zupan, Slovenia

For more information about the special issue please contact one of the special-issue editors:

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Peter Lucas  Inst. of Inform. and Computing Sciences  University of Nijmegen  Toernooiveld 1  6525 ED Nijmegen  The Netherlands  Telephone: +31 24 365 20 84  Fax: +31 24 365 3137  lucas@cs.uu.nl

Linda van der Gaag  Dept. of Comp. Science  Utrecht University  Padualaan 14  3584 TB Utrecht  The Netherlands  Telephone: +31 30 2534113  Fax: +31 30 2513791  linda@cs.uu.nl

Ameen Abu-Hanna Dept. of Medical Inform., AMC  University of Amsterdam  Meibergdreef 15  1105 AZ Amsterdam  The Netherlands  Telephone: +31 20 5664511  Fax : +31 20 6912432  A.Abu-Hanna@amc.uva.nl

Last updated: 19th January, 2002