Patient stratification with competing risks by multivariate Fisher distance

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2009, Pages 213-220

  Bacciu, Davide ^a   Jarman, Ian H ^b   Etchells, Terence A ^b   Lisboa, Paulo J G ^b  

^a UNIVERSITY OF PISA   (Italy)

^b LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AFFINE SPACE; AFFINE TRANSFORMATIONS; ARTIFICIAL NEURAL NETWORK; AUTOMATIC RELEVANCE DETERMINATION; CLUSTER NUMBER ESTIMATION; COMPETING RISKS; DATA SPACE; EVENT MODEL; FISHER DISTANCE; INDUCTION THERAPY; INPUT DATAS; MORTALITY RISK; SEMI-SUPERVISED; SURVIVAL MODEL;

NEURAL NETWORKS; RISK PERCEPTION; TOPOLOGY;

FISHER INFORMATION MATRIX;

EID: 70449394813     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2009.5179077     Document Type: Conference Paper

References (14)

1. F. Lo-Coco, A. Cuneo, F. Pane, D. Cilloni, D. Diverio, M. Mancini, N. Testoni, A. Bardi, B. Izzo, N. Bolli, R. L. Starza, P. Fazi, S. Iacobelli, A. Piciocchi, M. Vignetti, S. Amadori, F. Mandelli, P. Pelicci, C. Mecucci, B. Falini, and G. Saglio, "Prognostic impact of genetic characterization in the gimema lam99p multicenter study for newly diagnosed acute myeloid leukemia." Haematologica, vol. 93, no. 7, pp. 1017-1024, May 2008.
2. S. Kaski, J. Sinkkonen, and J. Peltonen, "Bankruptcy analysis with self-organizing maps in learning metrics, " Neural Networks, IEEE Transactions on, vol. 12, no. 4, pp. 936-947, Jul. 2001.
3. P. Lisboa, I. H. Jarman, T. A. Etchells, F. Ambrogi, I. Ardoino, M. Vignetti, and E. Biganzoli, "Short-term time-to-event model of response to treatment following a gimema protocol for acute myeloid leukaemia, " in "Computational Intelligence and Bio engineering", Workshop in honour of Antonina Starita, ser. Frontiers in Artificial Intelligence and Applications, F. Masulli, A. Micheli, and A. Sperduti, Eds. IOS Press, 2009 (To Appear).
4. E. Biganzoli, P. Boracchi, and E. Marubini, "A general framework for neural network models on censored survival data, " Neural Netw., vol. 15, no. 2, pp. 209-218, 2002.
5. E. Biganzoli, P. Boracchi, , and F. A. E. Marubini, "Artificial neural network for the joint modelling of discrete cause-specific hazards, " Artif lntell Med, vol. 37, no. 2, pp. 119-130, June 2006.
6. P. Lisboa, T. Etchells, I. Jarman, C. Arsenea, M. Aung, A. Eleuteri, A. F. G. Taktak, F. Ambrogi, P. Boracchi, and E. Biganzoli, "Partial logistic artificial neural network for competing risks regularised with automatic relevance determination, " Accepted for IEEE-TNN, 2009.
7. F. Ambrogi, E. Biganzoli, and P. Boracchi, "Estimates of clinically useful measures in competing risks survival analysis, " Statistics in Medicine, vol. 27, no. 30, pp. 6407-6425, 2008.
8. D. Bacciu and A. Starita, "Competitive repetition suppression (CoRe) clustering: A biologically inspired learning model with application to robust clustering, " Neural Networks, IEEE Transactions on, vol. 19, no. 11, pp. 1922-1941, Nov. 2008.
9. C. M. Bishop, Neural Networks for Pattern Recognition. New York, NY, USA: Oxford University Press, Inc., 1995.
10. D. J. C. MacKay, "The evidence framework applied to classification networks, " Neural Comput. , vol. 4, no. 5, pp. 720-736, 1992.
11. P. J. 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." Artif Intell Med, vol. 28, no. 1, pp. 1-25, May 2003.
12. S. Kaski and J. Sinkkonen, "Principle of learning metrics for exploratory data analysis, " J. VLSI Signal Process. Syst., vol. 37, no. 2-3, pp. 177-188, 2004.
13. B. Hammer and T. Villmann, "Generalized relevance learning vector quantization, " Neural Netw., vol. 15, no. 8-9, pp. 1059-1068, 2002.
14. S.-I. Amari, "Natural gradient works efficiently in learning, " Neural Comput. , vol. 10, no. 2, pp. 251-276, 1998.