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Notice

AISB miscellaneous Bulletin Item

Announcement - new book:Data Mining and Medical Knowledge Management: Cases and Applications

http://www.igi-global.com/reference/details.asp?id=33153

Data Mining and Medical Knowledge Management: Cases and Applications
ISBN: 978-1-60566-218-3; 464 pp; February 2009
Published under Medical Information Science Reference, an imprint of IGI Global
http://www.igi-global.com/reference/details.asp?id=33153
 
Edited by: Petr Berka, University of Economics, Prague, Czech Republic; Jan Rauch, University of Economics, Prague, Czech Republic; Djamel Abdelkader Zighed, University of Lumiere Lyon 2, France
 
DESCRIPTION
The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment.
 
Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.
 
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"This publication is a collection of case studies in which advanced DM and KM solutions are applied to concrete cases in biomedical research. The reader will find all the peculiarities of the medical field, which require specific solutions to complex problems."
- Riccardo Bellazzi, Universit di Pavia, Italy
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TOPICS COVERED
Classification and prediction with neural networks
Clinical prediction rules
Cost-sensitive learning in medicine
ECG processing
EEG data mining using PCA
Gene expression mining
Human embryo selection
Image registration for biomedical information integration
Medical knowledge management
Medical Web site management
Mining tuberculosis data
Ontologies in the health field
Preprocessing perceptrons and multivariate decision limits
Risk prediction models using data mining
 
For more information about Data Mining and Medical Knowledge Management: Cases and Applications, you can view the title information sheet at http://www.igi-global.com/downloads/pdf/33153.pdf. To view the Table of Contents and a complete list of contributors online go to http://www.igi-global.com/reference/details.asp?ID=33153&v=tableOfContents. You can also view the first chapter of the publication at http://www.igi-global.com/downloads/excerpts/33153.pdf
 
ABOUT THE EDITORS
Petr Berka is a full professor at the Department of Information and Knowledge Engineering, University of Economics and also works in the Centre of Biomedical Informatics, Institute of Computer Science, Academy of Sciences of the Czech Republic. His main research interests are machine learning, data mining and knowledge-based systems.
 
Jan Rauch is an associate professor at the Deptartment of Information and Knowledge Engineering, University of Economics and also works in the Centre of Biomedical Informatics, Institute of Computer Science, Academy of Sciences of the Czech Republic. His main research interest is data mining.
 
Djamel Abdelkader Zighed received his Master in Computer and Automatic Science in 1982 and his PhD in computer science in 1985  both from University Lyon I. He was as assistant professor at University Lyon 1 in 1984-1987. In 1987, he joined the University Lyon II where he worked as lecturer (1987-1991), professor (1991-2000) and 1st class professor (2000 - present). He is interested in data mining (including mining complex data), machine learning and knowledge engineering. He is the founder and was director (1995-2002) of the ERIC laboratory (laboratory for development of methods and software for knowledge engineering, and more specifically automatic Knowledge Discovery in Databases).