Methods Inf Med 2011; 50(06): 536-544
DOI: 10.3414/ME11-06-0002
Original Articles
Schattauer GmbH

Data Analysis and Data Mining: Current Issues in Biomedical Informatics

R. Bellazzi
1   Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
,
M. Diomidous
2   Department of Public Health, Faculty of Nursing, University of Athens, Athens, Greece
,
I. N. Sarkar
3   Center for Clinical and Translational Science, Department of Microbiology and Molecular Genetics, and Department of Computer Science, University of Vermont, Burlington, VT, USA
,
K. Takabayashi
4   Division of Medical Informatics and Management, Chiba University Hospital, Chiba, Japan
,
A. Ziegler
5   Institut für Medizinische Biometrie und Statistik, University of Luebeck, Luebeck, Germany
,
A. T. McCray
6   Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
22 January 2018 (online)

Preview

Summary

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.