Modelling survival prediction in medical data
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[1] Michael Negnevitsky,et al. Artificial Intelligence: A Guide to Intelligent Systems , 2001 .
[2] Lucila Ohno-Machado,et al. Journal of Biomedical Informatics , 2002 .
[3] T G Clark,et al. Survival Analysis Part I: Basic concepts and first analyses , 2003, British Journal of Cancer.
[4] David Collett. Modelling Survival Data in Medical Research , 1994 .
[5] Dursun Delen,et al. Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.
[6] Paulo J. G. Lisboa,et al. Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer , 2007, 2007 International Joint Conference on Neural Networks.
[7] Paulo J. G. Lisboa,et al. Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach , 2006, IEEE Transactions on Neural Networks.
[8] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[9] G. Clark,et al. A practical application of neural network analysis for predicting outcome of individual breast cancer patients , 2005, Breast Cancer Research and Treatment.
[10] Anthony C Fisher,et al. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem. , 2004, Physics in medicine and biology.
[11] F. Harrell,et al. Artificial neural networks improve the accuracy of cancer survival prediction , 1997, Cancer.
[12] Lionel Tarassenko,et al. Neural network models for breast cancer prognosis , 2005, Neural Computing & Applications.
[13] Paulo J. G. Lisboa,et al. A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.
[14] L Ohno-Machado,et al. A comparison of Cox proportional hazards and artificial neural network models for medical prognosis , 1997, Comput. Biol. Medicine.
[15] J. M. Jerez,et al. Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks , 2005, Breast Cancer Research and Treatment.
[16] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[17] P M Ravdin,et al. A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients. , 1999, Clinical cancer research : an official journal of the American Association for Cancer Research.
[18] S. Chabaud,et al. Breast Cancer Predictions by Neural Networks Analysis: a Comparison with Logistic Regression , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[19] B. Efron. Logistic Regression, Survival Analysis, and the Kaplan-Meier Curve , 1988 .
[20] Paulo J. G. Lisboa,et al. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer , 2003, Artif. Intell. Medicine.
[21] E Biganzoli,et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. , 1998, Statistics in medicine.
[22] Paulo J. G. Lisboa,et al. An integrated framework for risk profiling of breast cancer patients following surgery , 2008, Artif. Intell. Medicine.
[23] Chih-Lin Chi,et al. Application of Artificial Neural Network-Based Survival Analysis on Two Breast Cancer Datasets , 2007, AMIA.
[24] W. D. Ray. 4. Modelling Survival Data in Medical Research , 1995 .
[25] Ilias Maglogiannis,et al. Neural network-based diagnostic and prognostic estimations in breast cancer microscopic instances , 2006, Medical and Biological Engineering and Computing.
[26] D. Collett. Modelling survival data , 1994 .
[27] E. Wilkinson. Cancer Research UK , 2002 .
[28] Paulo J. G. Lisboa,et al. Are neural networks best used to help logistic regression? An example from breast cancer survival analysis , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[29] David S. Wishart,et al. Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.