Topics

In the workshop the attention will be given to methodological issues of intelligent data analysis and on specific applications in medicine, biomedicine and pharmacology. In terms of methodology, topics include, but are not limited to,

• data mining techniques, including machine learning, clustering, neural networks, etc.,
• other techniques for construction of predictive models,
• data visualization,
• analysis of large data sets,
• relational data mining,
• interpretation of time-ordered data (derivation and revision of temporal trends and other forms of temporal data abstraction),
• knowledge representation,
• knowledge management and its integration with intelligent data analysis techniques,
• utility of background knowledge in data analysis,
• integration of intelligent data analysis techniques within biomedical information systems.

A paper submitted to the workshop is expected to show a selected methodology can help to solve relevant problems in medicine, and would typically address the following issues:

• What is the medical or clinical problem addressed?
• Which knowledge representation was used?
• Was any prior knowledge available? How was this used in the data analysis or interpretation of results?
• How is/can the newly discovered knowledge put into use?

Contributions that discuss particular applications of intelligent data analysis techniques are invited, and can for example cover analysis of medical and health-care data, data coming from clinical bioinformatics data bases (like microarray data and DNA sequence analysis), analysis of pharmacological data, drug design, drug testing, and outcomes analysis.

We also invite papers on data analysis tools. Such papers can overview a particular tool and describe why and how this could be suitable for intelligent data analysis in medicine and other application areas that are a subject of the IDAMAP workshop. Preferably, the papers on data analysis tools would also describe a case study where the tool was used.