Clinical Applications of Artificial Neural Networks

doi:10.1017/CBO9780511543494

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.

‘… a useful and thought-provoking work which extends thoughts about brain function to deal with everyday decision-making.’

Source: Doctors.net

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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List of contributors
pp vii-x
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1 - Introduction
pp 1-28
- By Richard Dybowski, Vanya Gant
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Part I - Applications
pp 29-30
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2 - Artificial neural networks in laboratory medicine
pp 31-80
- By Simon S. Cross
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3 - Using artificial neural networks to screen cervical smears: how new technology enhances health care
pp 81-89
- By Mathilde E. Boon, Lambrecht P. Kok
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4 - Neural network analysis of sleep disorders
pp 90-101
- By Lionel Tarassenko, Mayela Zamora, James Pardey
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5 - Artificial neural networks for neonatal intensive care
pp 102-119
- By Emma A. Braithwaite, Jimmy Dripps, Andrew J. Lyon, Alan Murray
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6 - Artificial neural networks in urology: applications, feature extraction and user implementations
pp 120-142
- By Craig S. Niederberger, Richard M. Golden
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7 - Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy
pp 143-172
- By Royston Goodacre
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Part II - Prospects
pp 173-174
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8 - Recent advances in EEG signal analysis and classification
pp 175-191
- By Charles W. Anderson
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9 - Adaptive resonance theory: a foundation for ‘apprentice’ systems in clinical decision support?
pp 192-222
- By Robert F. Harrison, Simon S. Cross, R. Lee Kennedy, Chee Peng Lim
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10 - Evolving artificial neural networks
pp 223-234
- By V. William Porto, David B. Fogel
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Part III - Theory
pp 235-236
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11 - Neural networks as statistical methods in survival analysis
pp 237-255
- By Brian D. Ripley, Ruth M. Ripley
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12 - A review of techniques for extracting rules from trained artificial neural networks
pp 256-297
- By Robert Andrews, Alan B. Tickle, Joachim Diederich
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13 - Confidence intervals and prediction intervals for feedforward neural networks
pp 298-326
- By Richard Dybowski, Stephen J. Roberts
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Part IV - Ethics and clinical prospects
pp 327-328
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14 - Artificial neural networks: practical considerations for clinical application
pp 329-356
- By Vanya Gant, Susan Rodway, Jeremy Wyatt
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Index
pp 357-368
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Clinical Applications of Artificial Neural Networks

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

List of contributors
pp vii-x

1 - Introduction
pp 1-28

Part I - Applications
pp 29-30

2 - Artificial neural networks in laboratory medicine
pp 31-80

3 - Using artificial neural networks to screen cervical smears: how new technology enhances health care
pp 81-89

4 - Neural network analysis of sleep disorders
pp 90-101

5 - Artificial neural networks for neonatal intensive care
pp 102-119

6 - Artificial neural networks in urology: applications, feature extraction and user implementations
pp 120-142

7 - Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy
pp 143-172

Part II - Prospects
pp 173-174

8 - Recent advances in EEG signal analysis and classification
pp 175-191

9 - Adaptive resonance theory: a foundation for ‘apprentice’ systems in clinical decision support?
pp 192-222

10 - Evolving artificial neural networks
pp 223-234

Part III - Theory
pp 235-236

11 - Neural networks as statistical methods in survival analysis
pp 237-255

12 - A review of techniques for extracting rules from trained artificial neural networks
pp 256-297

13 - Confidence intervals and prediction intervals for feedforward neural networks
pp 298-326

Part IV - Ethics and clinical prospects
pp 327-328

14 - Artificial neural networks: practical considerations for clinical application
pp 329-356

Index
pp 357-368

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Clinical Applications of Artificial Neural Networks

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