Richard Dybowski / Vanya Gant (eds.)

Clinical Applications of Artificial Neural Networks

Herausgeber: Dybowski, Richard; Gant, Vanya

• Broschiertes Buch

Produktbeschreibung

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 cutting-edge volume. In addition to looking at applications the book looks forward to exciting future prospects on the horizon. 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 current and future prospects for harnessing the power of artificial neural networks in the investigation and treatment of disease.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 380
• Erscheinungstermin: 23. April 2007
• Englisch
• Abmessung: 244mm x 170mm x 20mm
• Gewicht: 655g
• ISBN-13: 9780521001335
• ISBN-10: 0521001331
• Artikelnr.: 22873434

Inhaltsangabe

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.