Table of Contents
Advances in Artificial Intelligence
Volume 2015, Article ID 270165, 7 pages
http://dx.doi.org/10.1155/2015/270165
Research Article

Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

1Department of Mathematics and Statistics, University of South Florida, 4202 E. Fowler Avenue, CMC 342, Tampa, FL 33620, USA
2Department of Mathematics and Statistics, University of South Florida, 4202 E. Fowler Avenue, CMC 366, Tampa, FL 33620, USA

Received 17 September 2014; Revised 17 February 2015; Accepted 23 February 2015

Academic Editor: Jun He

Copyright © 2015 Taysseer Sharaf and Chris P. Tsokos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. P. I. van Hinsbergen, J. W. C. van Lint, and H. J. van Zuylen, “Bayesian committee of neural networks to predict travel times with confidence intervals,” Transportation Research Part C: Emerging Technologies, vol. 17, no. 5, pp. 498–509, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. G. B. Kingston, M. F. Lambert, and H. R. Maier, “Bayesian training of artificial neural networks used for water resources modeling,” Water Resources Research, vol. 41, no. 12, Article ID W12409, pp. 1–11, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Baesens, T. van Gestel, M. Stepanova, D. van den Poel, and J. Vanthienen, “Neural network survival analysis for personal loan data,” Journal of the Operational Research Society, vol. 56, no. 9, pp. 1089–1098, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. D.-R. Chen, R.-F. Chang, W.-J. Kuo, M.-C. Chen, and Y.-L. Huang, “Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks,” Ultrasound in Medicine and Biology, vol. 28, no. 10, pp. 1301–1310, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Ercal, A. Chawla, W. V. Stoecker, H.-C. Lee, and R. H. Moss, “Neural network diagnosis of malignant melanoma from color images,” IEEE Transactions on Biomedical Engineering, vol. 41, no. 9, pp. 837–845, 1994. View at Publisher · View at Google Scholar · View at Scopus
  6. S.-J. Soong, S. Ding, D. Coit et al., “Predicting survival outcome of localized melanoma: an electronic prediction tool based on the AJCC melanoma database,” Annals of Surgical Oncology, vol. 17, no. 8, pp. 2006–2014, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society, Series B: Methodological, vol. 34, no. 2, pp. 187–220, 1972. View at Google Scholar · View at MathSciNet
  8. D. Faraggi and R. Simon, “A neural network model for survival data,” Statistics in Medicine, vol. 14, no. 1, pp. 73–82, 1995. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Biganzoli, P. Boracchi, L. Mariani, and E. Marubini, “Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach,” Statistics in Medicine, vol. 17, no. 10, pp. 1169–1186, 1998. View at Publisher · View at Google Scholar
  10. D. R. Mani, J. Drew, A. Betz, and P. Datta, “Statistics and data mining techniques for lifetime value modeling,” in Proceedings of the 5th ACM SIKGKDD International Conference on Knowledge Discovery and Data Mining (KDD '99), pp. 94–103, San Diego, Calif, USA, August 1999. View at Publisher · View at Google Scholar
  11. L. Bottaci, P. J. Drew, J. E. Hartley et al., “Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions,” The Lancet, vol. 350, no. 9076, pp. 469–472, 1997. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Lapuerta, S. P. Azen, and L. LaBree, “Use of neural networks in predicting the risk of coronary artery disease,” Computers and Biomedical Research, vol. 28, no. 1, pp. 38–52, 1995. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Ohno-Machado, “Sequential use of neural networks for survival prediction in AIDS,” in Proceedings of the AMIA Annual Symposium Proceedings Archive, pp. 170–174, 1996.
  14. P. M. Ravdin and G. M. Clark, “A practical application of neural network analysis for predicting outcome of individual breast cancer patients,” Breast Cancer Research and Treatment, vol. 22, no. 3, pp. 285–293, 1992. View at Publisher · View at Google Scholar · View at Scopus
  15. Surveillance Epidemiology and End Results (SEER) Program, Research Data (1973–2009), Division of Cancer Control and Population Sciences, National Cancer Institute, Surveillance Research Program, Surveillance Systems Branch, 2012, http://www.seer.cancer.gov/.
  16. F. Rosenblatt, The Perceptron: A Perceiving and Recognizing Automaton, Cornell Aeronautical Laboratory, 1957.
  17. P. D. Allison, “Discrete-time methods for the analysis of event histories,” Sociological Methodology, vol. 13, pp. 61–98, 1982. View at Publisher · View at Google Scholar
  18. J. D. Singer and J. B. Willett, “It's about time: using discrete-time survival analysis to study duration and the timing of events,” Journal of Educational and Behavioral Statistics, vol. 18, no. 2, pp. 155–195, 1993. View at Publisher · View at Google Scholar
  19. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995. View at MathSciNet
  20. W. N. Street, “A neural network model for prognostic prediction,” in Proceeding of the 15th International Conference on Machine Learning, San Francisco, Calif, USA, 1998.
  21. E. L. Kaplan and P. Meier, “Nonparametric estimation from incomplete observations,” Journal of the American Statistical Association, vol. 53, pp. 457–481, 1958. View at Publisher · View at Google Scholar · View at MathSciNet
  22. U. Anders and O. Korn, “Model selection in neural networks,” Neural Networks, vol. 12, no. 2, pp. 309–323, 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. B. D. Ripley, “Neural networks and flexible regression and discrimination,” Journal of Applied Statistics, vol. 21, no. 1-2, pp. 39–57, 1994. View at Publisher · View at Google Scholar
  24. B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, Cambridge, UK, 1996. View at Publisher · View at Google Scholar · View at MathSciNet
  25. P. J. G. Lisboa, H. Wong, P. Harris, and R. Swindell, “A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer,” Artificial Intelligence in Medicine, vol. 28, no. 1, pp. 1–25, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. D. J. C. MacKay, “Probable networks and plausible predictions—a review of practical Bayesian methods for supervised neural networks,” Network: Computation in Neural Systems, vol. 6, no. 3, pp. 469–505, 1995. View at Publisher · View at Google Scholar · View at Scopus
  27. D. E. Fisher and A. C. Geller, “Disproportionate burden of melanoma mortality in young US men: the possible role of biology and behavior,” JAMA Dermatology, vol. 149, no. 8, pp. 903–904, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. C. S. Gamba, C. A. Clarke, T. H. M. Keegan, L. Tao, and S. M. Swetter, “Melanoma survival disadvantage in young, non-hispanic white males compared with females,” JAMA Dermatology, vol. 149, no. 8, pp. 912–920, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. A. C. Society, “Cancer Facts & Figures 2013,” 2013, http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf.