Medical Informatics: Knowledge Management and Data Mining in Biomedicine / Edition 1
by Hsinchun Chen
Medical Informatics and biomedical computing have grown in quantum measure over the past decade. An abundance of advances have come to the foreground in this field with the vast amounts of biomedical and genomic data, the Internet, and the wide application of computer use in all aspects of medical, biological, and health care research and practice. MEDICAL
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Medical Informatics and biomedical computing have grown in quantum measure over the past decade. An abundance of advances have come to the foreground in this field with the vast amounts of biomedical and genomic data, the Internet, and the wide application of computer use in all aspects of medical, biological, and health care research and practice. MEDICAL INFORMATICS: Knowledge Management and Data Mining in Biomedicine covers the basic foundations of the area while extending the foundational material to include the recent leading-edge research in the field. The newer concepts, techniques, and practices of biomedical knowledge management and data mining are introduced and examined in detail. It is the research and applications in these areas that are raising the technical horizons and expanding the utility of informatics to an increasing number of biomedical professionals and researchers.
The book is divided into three major topical sections.
Section I presents the foundational information and knowledge management material and includes topics such as: bioinformatics challenges and standards, security and privacy, ethical and social issues, and biomedical knowledge mapping.
Section II discusses the topics which are relevant to knowledge representations & access and includes topics such as: representations of medical concepts and relationships, genomic information retrieval, 3D medical informatics, public access to anatomic images, and creating and maintaining biomedical ontologies.
Section III examines the emerging application research in data mining, biomedical textual mining, and knowledge discovery research and includes topics such as: semantic parsing and analysis for patient records, biological relationships, gene pathways, and metabolic networks, exploratory genomic data analysis, joint learning using data and text mining, and disease informatics and outbreak detection.
The book is a comprehensive presentation of the foundations and leading application research in medical informatics/biomedicine. These concepts and techniques are illustrated with detailed case studies.
The 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. In addition, individual expert contributing authors have been commissioned to write chapters for the book on their respective topical expertise.
Product Details
- ISBN-13:
- 9781441937353
- Publisher:
- Springer US
- Publication date:
- 11/05/2010
- Series:
- Integrated Series in Information Systems, #8
- Edition description:
- Softcover reprint of hardcover 1st ed. 2005
- Pages:
- 648
- Product dimensions:
- 6.10(w) x 9.20(h) x 1.50(d)
Table of Contents
Foundational Topics in Medical Informatics.- Knowledge Management, Data Mining, and Text Mining in Medical Informatics.- Mapping Medical Informatics Research.- Bioinformatics Challenges and Opportunities.- Managing Information Security and Privacy in Healthcare Data Mining.- Ethical and Social Challenges of Electronic Health Information.- Information and Knowledge Management.- Medical Concept Representation.- Characterizing Biomedical Concept Relationships.- Biomedical Ontologies.- Information Retrieval and Digital Libraries.- Modeling Text Retrieval in Biomedicine.- Public Access to Anatomic Images.- 3D Medical Informatics.- Infectious Diseaxe Informatics and Outbreak detection.- Text Mining and Data Mining.- Semantic Interpretation for the Biomedical Research Literature.- Semantic Text Parsing for Patient Records.- Identification of Biological Relationships from Text Documents.- Creating, Modeling, and Visualizing Metabolic Networks.- Gene Pathway Text Mining and Visualization.- The Genomic Data Mine.- Exploratory Genomic Data Analysis.- Joint Learning Using Multiple Types of Data and Knowledge.
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