Clinical Applications of Artificial Neural Networks de Richard Dybowski

Clinical Applications of Artificial Neural Networks

Richard Dybowski

Vanya Gant

en Limba Engleză Paperback – 11 iul 2007

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.

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	Preț	Express
Paperback (1)	600^.83 lei 6-8 săpt.
Cambridge University Press – 11 iul 2007	600^.83 lei 6-8 săpt.
Hardback (1)	1385^.16 lei 6-8 săpt.
Cambridge University Press – 8 aug 2001	1385^.16 lei 6-8 săpt.

Preț

Express

Paperback (1)

600^.83 lei 6-8 săpt.

Cambridge University Press – 11 iul 2007

600^.83 lei 6-8 săpt.

Hardback (1)

1385^.16 lei 6-8 săpt.

Cambridge University Press – 8 aug 2001

1385^.16 lei 6-8 săpt.

Cuprins

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.