Medical Data Mining and Knowledge Discovery / Edition 1
by Krzysztof J. CiosModern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data… See more details below
Overview
Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine. This book addresses all these specific features.
Product Details
- ISBN-13:
- 9783790824780
- Publisher:
- Physica-Verlag HD
- Publication date:
- 08/28/2015
- Series:
- Studies in Fuzziness and Soft Computing Series, #60
- Edition description:
- Softcover reprint of hardcover 1st ed. 2001
- Pages:
- 498
Table of Contents
K.J. Cios, G.W. Moore: Medical Data Mining and Knowledge Discovery: Overview of Key Issues.- J.M. Saul: Legal Policy and Security Issues in the Handling of Medical Data.- W. Ceusters: Medical Natural Language Understanding as a Supporting Technology for Data Mining in Healthcare.- G.W. Moore, J.J. Berman: Anatomic Pathology Data Mining.- D. Shalvi, N. DeClaris: A Data Clustering and Visualization Methodology for Epidemiological Pathology Discoveries.- V. Megalooikonomou, E.H. Herskovits: Mining Structure-Function Associations in a Brain Image Database.- K.G. Goh, W. Hsu, M.L. Lee, H. Wang: ADRIS: An Automatic Diabetic Retinal Image Screening System.- M. Last, O. Maimon, A. Kandel: Knowledge Discovery in Mortality Records: An Info-Fuzzy Approach.- B. Kovalerchuk, E. Vityaev, J.F. Ruiz: Consistent and Complete Data and "Expert" Mining in Medicine.- M.L. Wong, W. Lam, K.S. Leung: A Medical Data Mining Application Based on Evolutionary Computation.- S. Miksch, A. Seyfang, W. Horn, C. Popow, F. Paky: Methods of Temporal Data Validation and Abstraction in High-Frequency Domains.- G.B. Singh, S. Krawetz: Data Mining the Matrix Associated Regions (MARs) for Gene Therapy.- J.C.G. Ramirez, L.L. Peterson, D.J. Cook, D.M. Peterson: Discovery of Temporal Patterns in Sparse Course-of-Disease Data.- L.A. Consularo, R. de Alencar Lotufo, L. da Fontoura Costa: Data Mining-Based Modeling of Human Visual Perception.- S. Tsumoto: Discovery of Clinical Knowledge in Databases Extracted from Hospital Information Systems.- F. Alonso, J.P. Caraca-Valente, I. López-Chavarrías, C. Montes: Knowledge Discovery in Time Series Using Expert Knowledge.Customer Reviews
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