Summary
Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent
Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007).
Methods: Defining the field of biomedical data mining. Characterizing current developments
and challenges for researchers in the field. Reporting on current and future activities
of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the
content of the selected papers in this special topic.
Results and Conclusions: In the biomedical field, data mining methods are used to develop clinical diagnostic
and prognostic systems, to interpret biomedical signal and image data, to discover
knowledge from biological and clinical databases, and in biosurveillance and anomaly
detection applications. The main challenges for the field are i) dealing with very
large search spaces in a both computationally efficient and statistically valid manner,
ii) incorporating and utilizing medical and biological background knowledge in the
data analysis process, iii) reasoning with time-oriented data and temporal abstraction,
and iv) developing end-user tools for interactive presentation, interpretation, and
analysis of large datasets.
Keywords
Data mining - machine learning