| Artificial Neural Networks in Biomedicine 2000 Edition Contributor(s): Lisboa, Paulo J. G. (Editor), Ifeachor, Emmanuel C. (Editor), Szczepaniak, Piotr S. (Editor) |
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ISBN: 1852330058 ISBN-13: 9781852330057 Publisher: Springer OUR PRICE: $104.49 Product Type: Paperback Published: February 2000 Annotation: This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas. It looks at biomedical applications which involve customising neural network technology to resolve specific difficulties with data processing, and deals with applications relating to particular aspects of clinical practice and laboratory or medically-related analysis. Each chapter is self-contained with regard to the technology used, covering important technical points and implementation issues like the design of user interfaces and hardware/software platforms. Artificial Neural Networks in Biomedicine will be of interest to computer scientists and neural network practitioners who want to extend their knowledge of issues relevant to biomedical applications, developers of clinical computer systems, and medical researchers looking for new methods and computational tools. |
| Additional Information |
| BISAC Categories: - Medical | Family & General Practice - Computers | Intelligence (ai) & Semantics - Medical | Administration |
| Dewey: 610.285 |
| LCCN: 99029082 |
| Series: Perspectives in Neural Computing |
| Physical Information: 0.72" H x 6.12" W x 9.08" (1.00 lbs) 288 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Following the intense research activIties of the last decade, artificial neural networks have emerged as one of the most promising new technologies for improving the quality of healthcare. Many successful applications of neural networks to biomedical problems have been reported which demonstrate, convincingly, the distinct benefits of neural networks, although many ofthese have only undergone a limited clinical evaluation. Healthcare providers and developers alike have discovered that medicine and healthcare are fertile areas for neural networks: the problems here require expertise and often involve non-trivial pattern recognition tasks - there are genuine difficulties with conventional methods, and data can be plentiful. The intense research activities in medical neural networks, and allied areas of artificial intelligence, have led to a substantial body of knowledge and the introduction of some neural systems into clinical practice. An aim of this book is to provide a coherent framework for some of the most experienced users and developers of medical neural networks in the world to share their knowledge and expertise with readers. |