Clinical Applications of Artificial Neural Networks 1st Edition by Richard Dybowski, Vanya Gant ISBN 0521001331 9780521001335

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Product details:

ISBN 10: 0521001331

ISBN 13: 9780521001335 

Author: Richard Dybowski, Vanya Gant

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.

Table of contents:

1. List of contributors

2. Introduction

3. Applications

4. Artificial neural networks in laboratory medicine

5. Using artificial neural networks to screen cervical smears: how new technology enhances health care

6. Neural network analysis of sleep disorders

7. Artificial neural networks for neonatal intensive care

8. Artificial neural networks in urology: applications, feature extraction and user implementations

9. Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy

10. Prospects

11. Recent advances in EEG signal analysis and classification

12. Adaptive resonance theory: a foundation for ‘apprentice’ systems in clinical decision support?

13. Evolving artificial neural networks

14. Theory

15. Neural networks as statistical methods in survival analysis

16. A review of techniques for extracting rules from trained artificial neural networks

17. Confidence intervals and prediction intervals for feedforward neural networks

18. Ethics and Clinical Prospects

19. Artificial neural networks: practical considerations for clinical application

20. Index

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Tags: Richard Dybowski, Vanya Gant, Clinical Applications, Artificial Neural Networks