Deep multi-task Gaussian processes for survival analysis with competing risks

Advances in Neural Information Processing Systems

Volumn 2017-December, Issue , 2017, Pages 2330-2338

  Alaa, Ahmed M ^a   Van Der Schaar, Mihaela ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; BIOINFORMATICS; DATA HANDLING; DIAGNOSIS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); HEALTH RISKS; INFERENCE ENGINES; PATIENT TREATMENT;

CAUSE-SPECIFIC SURVIVAL; ELECTRONIC HEALTH RECORD; GAUSSIAN PROCESSES; NON-PARAMETRIC BAYESIAN MODELING; NONLINEAR INTERACTIONS; OPTIMAL TREATMENT; SYNTHETIC AND REAL DATA; VARIATIONAL INFERENCE;

RISK ASSESSMENT;

EID: 85046998540     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. H. J. Lim, X. Zhang, R. Dyck, and N. Osgood. Methods of Competing Risks Analysis of End-stage Renal Disease and Mortality among People with Diabetes. BMC Medical Research Methodology, 10(1), 97, 2010.
2. P. C. Lambert, P. W. Dickman, C. P. Nelson, and P. Royston. Estimating the Crude Probability of Death due to Cancer and other Causes using Relative Survival Models. Statistics in Medicine, 29(7): 885-895, 2010.
3. J. M. Satagopan, L. Ben-Porat, M. Berwick, M. Robson, D. Kutler, and A. Auerbach. A Note on Competing Risks in Survival Data Analysis. British Journal of Cancer, 91(7): 1229-1235, 2004.
4. J. P. Fine and R. J. Gray. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American statistical association, 94(446): 496-509, 1999.
5. M. J. Crowder. Classical competing risks. CRC Press, 2001.
6. T. A. Gooley, W. Leisenring, J. Crowley, and B. E. Storer. Estimation of Failure Probabilities in the Presence of Competing Risks: New Representations of Old Estimators. Statistics in Medicine, 18(6): 695-706, 1999.
7. A. Tsiatis. A Non-identifiability Aspect of the Problem of Competing Risks. PNAS, 72(1): 20-22, 1975.
8. J. Henry, Y. Pylypchuk, T. Searcy, and V. Patel. Adoption of Electronic Health Record Systems among US Non-federal Acute Care Hospitals: 2008-2015. The Office of National Coordinator, 2016.
9. T. Fernndez, N. Rivera, and Y. W. Teh. Gaussian Processes for Survival Analysis. In NIPS, 2016.
10. C. N. Yu, R. Greiner, H. C. Lin, and V. Baracos. Learning Patient-specific Cancer Survival Distributions as a Sequence of Dependent Regressors. In NIPS, 1845-1853, 2011.
11. H. Steck, B. Krishnapuram, C. Dehing-oberije, P. Lambin and V. C. Raykar. On Ranking in Survival Analysis: Bounds on the Concordance Index. In NIPS, 1209-1216, 2008.
12. R. Ranganath, A. Perotte, N. Elhadad, and D. Blei. Deep Survival Analysis. arXiv:1608.02158, 2016.
13. F. S. Collins and H. Varmus. A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9): 793-795, 2015.
14. M. A. Alvarez, L. Rosasco, N. D. Lawrence. Kernels for Vector-valued Functions: A Review. Foundations and Trends ® R in Machine Learning, 4(3):195-266, 2012.
15. A. Damianou and N. Lawrence. Deep Gaussian Processes. In AISTATS, 2013.
16. E. V. Bonilla, K. M. Chai, and C. Williams. Multi-task Gaussian Process Prediction. In NIPS, 2007.
17. M. K. Titsias and N. D. Lawrence. Bayesian Gaussian Process Latent Variable Model. In AISTATS, 2010.
18. D. Kingma and J. Ba. ADAM: A Method for Stochastic Optimization. arXiv:1412.6980, 2014.
19. J. E. Barrett and A. C. C. Coolen. Gaussian Process Regression for Survival Data with Competing Risks. arXiv preprint arXiv:1312.1591, 2013.
20. E. Snelson, C. E. Rasmussen, and Z. Ghahramani. Warped Gaussian Processes. In NIPS, 2004.
21. E. L. Kaplan and P. Meier. Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282):457-481, 1958.
22. D. Cox. Regression Models and Life-tables. Journal of Royal Statistical Society, 34(2):187-220, 1972.
23. M. De Iorio, W. O. Johnson, P. Mller, and G. L. Rosner. Bayesian Nonparametric Non-proportional Hazards Survival Modeling. Biometrics, 65(3): 762-771, 2009.
24. S. Martino, R. Akerkar, and H. Rue. Approximate Bayesian Inference for Survival Models. Scandinavian Journal of Statistics, 38(3):514-528, 2011.
25. M. L. T. Lee and A. G. Whitmore. Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary. Statistical Science, 501-513, 2006.
26. H. Ishwaran, U. B. Kogalur, E. H. Blackstone, and M. S. Lauer. Random Survival Forests. The Annals of Applied Statistics, 841-860, 2008.
27. M. Wolbers, P. Blanche, M. T. Koller, J. C. Witteman and A. T. Gerds. Concordance for Prognostic Models with Competing Risks. Biostatistics, 15(3): 526-539, 2014.
28. P. C. Austin, D. S. Lee, and J. P. Fine. Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation, 133(6): 601-609, 2016.
29. R. Koene, et al Shared Risk Factors in Cardiovascular Disease and Cancer. Circulation, 2016.