Data mining and medical knowledge management

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  • Current research directions are looking at Data Mining (DM) and Knowledge Management (KM) as complementary and interrelated felds, aimed at supporting, with algorithms and tools, the lifecycle of knowledge, including its discovery, formalization, retrieval, reuse, and update. While DM focuses on the extraction of patterns, information, and ultimately knowledge from data (Giudici, 2003; Fayyad et al.

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  • The field of medical informatics has grown rapidly over the past decade due to the advances in biomedical computing, the abundance of biomedical and genomic data, the ubiquity of the Internet, and the general acceptance of computing in various aspects of medical, biological, and health care research and practice. This book aims to be complementary to several other popular introductory medical informatics textbooks.

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  • The field of medical informatics has grown rapidly over the past decade due to the advances in biomedical computing, the abundance of biomedical and genomic data, the ubiquity of the Internet, and the general acceptance of computing in various aspects of medical, biological, and health care research and practice. This book aims to be complementary to several other popular introductory medical informatics textbooks.

    pdf656p hyperion75 22-01-2013 32 4   Download

  • The biological sciences have become more quantitative and information-driven since emerging computational and mathematical tools facilitate collection and analysis of vast amounts of biological data. Complexity analysis of biological systems provides biological knowledge for the organization, management, and mining of biological data by using advanced computational tools. The biological data are inherently complex, nonuniform, and collected at multiple temporal and spatial scales.

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