R. Bellazzi (1), M. Diomidous (2), I. N. Sarkar (3), K. Takabayashi (4), A. Ziegler (5), A. T. McCray (6)
(1) Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy; (2) Department of Public Health, Faculty of Nursing, University of Athens, Athens, Greece; (3) Center for Clinical and Translational Science, Department of Microbiology and Molecular Genetics, and Department of Computer Science, University of Vermont, Burlington, VT, USA; (4) Division of Medical Informatics and Management, Chiba University Hospital, Chiba, Japan; (5) Institut für Medizinische Biometrie und Statistik, University of Luebeck, Luebeck, Germany; (6) Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Background: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.
Data Mining, biomedical informatics, data analysis, Translational bioinformatics, data-driven methods
R. A. Greenes, S. Panchanathan, V. Patel, H. Silverman, E. H. Shortliffe
IMIA Yearbook 2008 3: 150-156
Reinhold Haux (President of IMIA 2007-2010)1
IMIA Yearbook 2008 3: 1-6
IMIA Yearbook 2007 2: 186-191
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