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.

Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.

This book presents six thoroughly reviewed and revised full papers describing selected projects on data mining. Three papers deal with data mining and e-commerce, focusing on sequence rule analysis, association rule mining and knowledge discovery in databases, and intelligent e-marketing with Web mining. One paper is devoted to experience management and process learning. The last two papers report on medical applications, namely on genomic data processing and on case-based reasoning for prognosis of influenza.

This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

The Industrial Conference on Data Mining ICDM-Leipzig was the fourth meeting in a series of annual events which started in 2000, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. The mission of the conference is to bring together researchers and people from industry in order to discuss together new trends and applications in data mining. This year a broad spectrum of work of different applications was presented ranging from image mining, medicine and biotechnology, management and environmental control, to telecommunications. Besides that an industrial exhibition showed the successful application of data mining methods by industries in different areas such as medical devices, mass data management systems, data mining tools, etc. During the discussion many projects were inspired leading to new and joint work. The fruitful discussions, the exchange of ideas and the spirit of the conference made it a remarkable event for both sides, industry and research. We would like to express our appreciation to the reviewers for their precise and highly professional work. We appreciate the help and understanding of the editorial staff at Springer and in particular Alfred Hofmann, who supported the publication of these proceedings in the LNAI series. Last, but not least, we wish to thank all speakers, participants and industrial exhibitors who contributed to the success of the conference. We are looking forward to welcoming you to ICDM 2005 (www.data-mini- forum.de) and to the new work you will present there.

Data mining, or knowledge discovery in databases (KDD), is one of the fastest growing areas in computing application: it offers powerful tools to analyze the many large data bases used in business, science, and industry. Data mining technology searches large databases to extract information and patterns that can be translated into useful applications, such as classifying or predicting customer behavior. This book brings together fundamental knowledge on all aspects of data mining--concepts, theory, techniques, applications, and case studies. Designed for students and professionals in such fields as computing applications, information systems management and strategic research and management, the Handbook is a comprehensive guide to essential tools and technology, from neural networks to artificial intelligence. There is a strong emphasis on real-world case studies in such areas as banking, finance, marketing management, real estate, engineering, medicine, pharmacology, and the biosciences. A much needed resource on one of the fastest growing areas of computer applications--the development and use of tools to analyze, interpret, and make use of the enormous amounts of information stored in the world's databases.

This publication reflects the objective of the conference to highlight large scale projects supporting the use of information and communication technology (eHealth) at national, regional, and also at international level. It results in requirements for national and regional solutions for medical informatics and health information management.