000			03924nam a22003858i 4500
001			CR9780511543494
003			UkCbUP
005			20200124160313.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090505s2001||||enk o ||1 0|eng|d
020			_a9780511543494 (ebook)
020			_z9780521662710 (hardback)
020			_z9780521001335 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aR853.D37 _bC535 2001
082	0	0	_a616/.00285/632 _221
245	0	0	_aClinical applications of artificial neural networks / _cedited by Richard Dybowski and Vanya Gant.
264		1	_aCambridge : _bCambridge University Press, _c2001.
300			_a1 online resource (ix, 368 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_a1. Introduction / Richard Dybowski and Vanya Gant -- Part I. Applications -- 2. Artifical neural networks in laboratory medicine / Simon S. Cross -- 3. Using artifical 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 [and others] -- 6. Artificial neural networks in urology: applications, feature extraction and user implementations / Craig S. Niederberger and Richard M. Golden -- 7. Artificial neural lnetworks as a tool for whole organism fingerprinting in bacterial taxonomy / Royston Goodacre -- Part I. Prospects -- 8. Recent advances in EEG signal analysis and classification / Charles W. Anderson and David A. Peterson.
505	0		_a9. Adaptive resonance theory: a foundation for 'apprentice' systems in clinical decison support? / Robert F. Harrison [and others] -- 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.
520			_aArtificial 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.
650		0	_aMedicine _xResearch _xData processing.
650		0	_aNeural networks (Computer science)
650		0	_aClinical medicine _xDecision making _xData processing.
700	1		_aDybowski, Richard, _d1951- _eeditor.
700	1		_aGant, Vanya, _eeditor.
776	0	8	_iPrint version: _z9780521662710
856	4	0	_uhttps://doi.org/10.1017/CBO9780511543494
999			_c521272 _d521270