E-Science and artificial neural networks in cancer management

Concurrency and Computation: Practice and Experience

Volumn 19, Issue 2, 2007, Pages 251-263

  Dolgobrodov, S D ^a   Marshall, R ^a   Moore, P ^a   Bittern, R ^b   Steele, R J C ^b   Cuschieri, A ^c  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF DUNDEE   (United Kingdom)

^c SCUOLA SUPERIORE SANT ANNA   (Italy)

Author keywords

Artificial neural network; Cancer management; e Science

Indexed keywords

COMPUTER SCIENCE; HEALTH CARE; INFORMATION MANAGEMENT; INTERFACES (COMPUTER); NEURAL NETWORKS; PATIENT TREATMENT; WORLD WIDE WEB;

CANCER MANAGEMENT; E-SCIENCE; PATIENT DATA; WEB-BASED INTERFACE;

ONCOLOGY;

EID: 33846558985     PISSN: 15320626     EISSN: 15320634     Source Type: Journal    
DOI: 10.1002/cpe.1045     Document Type: Conference Paper

References (19)

1. http://www.pparc.ac.uk/Rs/Fs/Es/e_science_first_call.asp [5 December 2005].
2. http://www.nesc.ac.uk/nesc/define.html [5 December 2005].
3. http://www.nerc.ac.uk/funding/escience/ESci_AO2_Framework.shtml [5 December 2005].
4. Dybowski R. Neural computation in medicine: Perspectives and prospects. Proceedings of the ANNIMAB-1 Conference (Artificial Neural Networks in Medicine and Biology), May 2000, Malmgren H, Borga M, Niklasson L (eds.). Springer: Berlin, 2000; 26-31.
5. Lisboa PJG. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks 2002; 15(1):11-39.
6. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346:1135-1142.
7. Naguib RN et al. Artificial neural networks in cancer research. Pathobiology 1997; 65(3):129-157.
8. Biganzoli E et al. Feed forward neural networks for the analysis of censored survival data: A partial logistic regression approach. Statistics and Medicine 1998; 17:1169-1174.
9. Lisboa PJG, Wong H, Harris P, Swindell R. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artificial Intelligence in Medicine 2003; 28:1-5.
10. Ripley BD, Ripley RM. Neural networks as statistical methods in survival analysis. Clinical Applications of Artificial Neural Networks. Cambridge University Press: New York, 2001.
11. Lowe D, Webb AR. Exploiting prior knowledge in network optimization: An illustration from medical prognosis. Network 1990; 1:299-321.
12. Lisboa PJG, Vellido A, Wong H. Bias reduction in skewed binary classification with Bayesian neural networks. Neural Networks 2000; 13:407-416.
13. Tarassenko L. A Guide to Neural Computing Applications. Wiley: London, 1998.
14. Collet D. Modelling Survival Data In Medical Research. Chapman and Hall: London, 1994.
15. Egan JP. Signal Detection Theory and ROC Analysis. Academic Press: New York, 1975.
16. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29-64.
17. Gridsite. http://www.gridsite.org [5 December 2005].
18. Schwarzer G, Vach W, Schumacher WM. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Statistics and Medicine 2001; 19:541-561.
19. Bishop CM. Neural Networks for Pattern Recognition. Clarendon Press: Oxford, 1995.