Clinical Applications of Artificial Neural Networks

by Richard Dybowski, Vanya Gant

Overview

Artificial neural networks provides a powerful tool to help doctors analyze, model, and make sense of complex clinical data across a broad range of medical applications. Their potential in clinical medicine is reflected in the diversity of topics covered in this cutting-edge volume. In addition to looking at new and forthcoming applications the book looks forward to exciting future prospects on the horizon. The volume also examines ethical and legal concerns about the use of "black-box" systems as decision aids in medicine. This eclectic collection of chapters provides an exciting overview of current and future prospects for harnessing the power of artificial neural networks in the investigation and treatment of disease.

Product Details

ISBN-13:	9780521001335
Publisher:	Cambridge University Press
Publication date:	07/12/2007
Pages:	380
Product dimensions:	6.65(w) x 9.57(h) x 0.75(d)

About the Author

Dybowski, Richard; Gant, Vanya

Table of Contents

List of contributors; 1. Introduction Richard Dybowski and Vanya Gant; Part I. Applications: 2. Artificial neural networks in laboratory medicine Simon S. Cross; 3. Using artificial neural networks to screen cervical smears: how new technology enhances health care Mathilde E. Boon and Lambrecht P. Kok; 4. Neural network analysis of sleep disorders Lionel Tarassenko, Mayela Zamora and James Pardey; 5. Artificial neural networks for neonatal intensive care Emma A. Braithwaite, Jimmy Dripps, Andrew J. Lyon and Alan Murray; 6. Artificial neural networks in urology: applications, feature extraction and user implementations Craig S. Niederberger and Richard M. Golden; 7. Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy Royston Goodacre; Part II. Prospects: 8. Recent advances in EEG signal analysis and classification Charles W. Anderson and David A. Peterson; 9. Adaptive resonance theory: a foundation for 'apprentice' systems in clinical decision support? Robert F. Harrison, Simon S. Cross, R. Lee Kennedy, Chee Peng Lim and Joseph Downs; 10. Evolving artificial neural networks V. William Porto and David B. Fogel; Part III. Theory: 11. Neural networks as statistical methods in survival analysis Brian D. Ripley and Ruth M. Ripley; 12. A review of techniques for extracting rules from trained artificial neural networks Robert Andrews, Alan B. Tickle and Joachim Diederich; 13. Confidence intervals and prediction intervals for feedforward neural networks Richard Dybowski and Stephen J. Roberts; Part IV. Ethics and Clinical Prospects: 14. Artificial neural networks: practical considerations for clinical application Vanya Gant, Susan Rodway and Jeremy Wyatt; Index.

Editorial Reviews

"...broad and comprehensive...well-written..." Statistical Methods in Medical Research

"The book is rich in reviews...a useful and thought-provoking work which extends thoughts about brain function to deal with everyday decision-making." Doctors.net

From the Publisher