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Clinical Applications of Artificial Neural Networks

Clinical Applications of Artificial Neural Networks

9780521001335
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Description
Artificial neural networks provide a powerful tool to help doctors analyse, 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 volume. In addition to looking at applications the book looks forward to exciting future prospects. A section on theory looks at approaches to validate and refine the results generated by artificial neural networks. The volume also recognizes that concerns exist about the use of black-box systems as decision aids in medicine, and the final chapter considers the ethical and legal conundrums arising out of their use for diagnostic or treatment decisions. Taken together, this eclectic collection of chapters provides an exciting overview of harnessing the power of artificial neural networks in the investigation and treatment of disease.
Product Details
Cambridge University Press
97045
9780521001335
9780521001335

Data sheet

Publication date
2007
Issue number
1
Cover
paperback
Pages count
380
Dimensions (mm)
169.00 x 243.00
Weight (g)
623
  • 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.
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