Abstract
Survival analysis aims to predict the occurring times of certain events of interest. Most existing methods for survival analysis either assume specific forms for the underlying stochastic processes or linear hypotheses. To cope with non-linearity in data, we propose a unified framework that combines multi-task and kernel learning for survival analysis. We also develop optimization methods based on the Pegasos (Primal estimated sub-gradient solver for SVM) algorithm for learning. Experiment results demonstrate the effectiveness of the proposed method for survival analysis, on synthetic and real-world data sets.
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References
Alaa, A.M., van der Schaar, M.: Deep multi-task Gaussian processes for survival analysis with competing risks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 2326–2334. Curran Associates Inc. (2017)
Cox, D.R.: Regression models and life-tables. J. Roy. Stat. Soc. Ser. B (Methodological) 34(2), 187–202 (1972)
Fernández, T., Rivera, N., Teh, Y.W.: Gaussian processes for survival analysis. In: Advances in Neural Information Processing Systems, pp. 5021–5029 (2016)
Harrell, F.E., Califf, R.M., Pryor, D.B., Lee, K.L., Rosati, R.A.: Evaluating the yield of medical tests. JAMA 247(18), 2543–2546 (1982)
Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S., et al.: Random survival forests. Ann. Appl. Stat. 2(3), 841–860 (2008)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958)
Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)
Kimeldorf, G., Wahba, G.: Some results on tchebycheffian spline functions. J. Math. Anal. Appl. 33(1), 82–95 (1971)
Knaus, W.A.: The support prognostic model: objective estimates of survival for seriously ill hospitalized adults. Ann. Internal Med. 122(3), 191–203 (1995)
Lee, C., Zame, W.R., Yoon, J., van der Schaar, M.: Deephit: a deep learning approach to survival analysis with competing risks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Lee, E.T., Wang, J.: Statistical Methods for Survival Data Analysis, vol. 476. Wiley, New York (2003)
Li, H., Ge, Y., Zhu, H., **ong, H., Zhao, H.: Prospecting the career development of talents: a survival analysis perspective. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 917–925. ACM (2017)
Li, Y., Wang, J., Ye, J., Reddy, C.K.: A multi-task learning formulation for survival analysis. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1715–1724. ACM (2016)
Li, Z., et al.: Water pipe condition assessment: a hierarchical beta process approach for sparse incident data. Mach. Learn. 95(1), 11–26 (2013). https://doi.org/10.1007/s10994-013-5386-z
Lin, P., et al.: Data driven water pipe failure prediction: a bayesian nonparametric approach. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 193–202. ACM (2015)
Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1), 3–30 (2011)
Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for cox’s proportional hazards model via coordinate descent. J. Stat. Software 39(5), 1 (2011)
Tibshirani, R.: The lasso method for variable selection in the cox model. Stat. Med. 16(4), 385–395 (1997)
Zhang, Q., Zhou, M.: Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks. In: Advances in Neural Information Processing Systems, pp. 5002–5013 (2018)
Zhou, J., Yuan, L., Liu, J., Ye, J.: A multi-task learning formulation for predicting disease progression. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 814–822. ACM (2011)
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Meng, Z., Xu, J., Li, Z., Wang, Y., Chen, F., Wang, Z. (2021). A Multi-task Kernel Learning Algorithm for Survival Analysis. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_24
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DOI: https://doi.org/10.1007/978-3-030-75768-7_24
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