Abstract
In the paper the ensemble of dipolar neural networks (EDNN) for analysis of survival data is proposed. The tool is build on the base of the learning sets, which contain the data from clinical studies following patients response for a given treatment. Such datasets may contain incomplete (censored) information on patients failure times. The proposed method is able to cope with censored observations and as the result returns the aggregated Kaplan-Meier survival function. The prediction ability of the received tool as well as the significance of individual features is verified by the Brier score, \(\tilde{D}_{S,x}\) and \(\hat{D}_x\) measures of predictive accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bobrowski, L., Krȩtowska, M., Krȩtowski, M.: Design of neural classifying networks by using dipolar criterions. In: Proc. of the Third Conference on Neural Networks and Their Applications, Kule, Poland, pp. 689–694 (1997)
Breiman, L.: How to use survival forest, http://stat-www.berkeley.edu/users/breiman
Cox, D.R.: Regression models and life tables (with discussion). Journal of the Royal Statistical Society B 34, 187–220 (1972)
Fleming, T.R., Harrington, D.P.: Counting Processes and Survival Analysis. John Wiley & Sons, Inc., Chichester (1991)
Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M.: Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 18, 2529–2545 (1999)
Hothorn, T., Lausen, B., Benner, A., Radespiel-Troger, M.: Bagging survival trees. Statistics in Medicine 23, 77–91 (2004)
Hothorn, T., Buhlmann, P., Dudoit, S., Molinaro, A.M., van der Laan, M.J.: Survival ensembles. Berkeley Division of Biostatistics Working Paper Series, vol. 174 (2005), http://www.bepress.com/ucbbiostat/paper174
Kalbfleisch, J.D., Prentice, R.L.: The statistical analysis of failure time data. John Wiley & Sons, New York (1980)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 5, 457–481 (1958)
Krȩtowska, M.: Dipolar regression trees in survival analysis. Biocybernetics and biomedical engineering 24(3), 25–33 (2004)
Krȩtowska, M., Bobrowski, L.: Artificial Neural Networks in Identifying Areas with Homogeneous Survival Time. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1008–1013. Springer, Heidelberg (2004)
Krętowska, M.: Random Forest of Dipolar Trees for Survival Prediction. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 909–918. Springer, Heidelberg (2006)
Ridgeway, G.: The state of boosting. Computing Science and Statistics 31, 1722–1731 (1999)
Schemper, M.: Predictive accuracy and explained variation. Statistics in Medicine 22, 2299–2308 (2003)
Schemper, M., Henderson, R.: Predictive accuracy and explained variation in Cox regression. Biometrics 56, 249–255 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krȩtowska, M. (2008). Ensemble of Dipolar Neural Networks in Application to Survival Data. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-69731-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
eBook Packages: Computer ScienceComputer Science (R0)