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,
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 management and its integration with intelligent data
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.