Multi-source survival domain adaptation
Article No.: 1096, Pages 9752 - 9762
Abstract
Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patients is better than another. With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times. To succeed with that, there are three crucial issues to be tackled. First, some patient data is censored: we do not know the true survival times for all patients. Second, data is scarce, which led past research to treat different illness types as domains in a multi-task setup. Third, there is the need for adaptation to new or extremely rare illness types, where little or no labels are available. In contrast to previous multi-task setups, we want to investigate how to efficiently adapt to a new survival target domain from multiple survival source domains. For this, we introduce a new survival metric and the corresponding discrepancy measure between survival distributions. These allow us to define domain adaptation for survival analysis while incorporating censored data, which would otherwise have to be dropped. Our experiments on two cancer data sets reveal a superb performance on target domains, a better treatment recommendation, and a weight matrix with a plausible explanation.
References
[1]
Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; and Vaughan, J. W. 2010. A theory of learning from different domains. Machine learning, 79(1): 151-175.
[2]
Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. 2007. Analysis of representations for domain adaptation. In NeurIPS, 137-144.
[3]
Cicirello, V. A. 2019. Kendall tau sequence distance: Extending Kendall tau from ranks to sequences. arXiv preprint arXiv:I905.02752.
[4]
Cortes, C.; and Mohri, M. 2014. Domain adaptation and sample bias correction theory and algorithm for regression. Theoretical Computer Science, 519: 103-126.
[5]
Cox, D. R. 1972. Regression models and life tables. Journal of the Royal Statistical Society B, 34: 187-220.
[6]
Cox, D. R.; and Oakes, D. 1984. Analysis of Survival Data. London, UK: Chapman & Hall.
[7]
Fernandez, T.; and Gretton, A. 2019. A maximum-mean-discrepancy goodness-of-fit test for censored data. In The 22nd International Conference on Artificial Intelligence and Statistics, 2966-2975. PMLR.
[8]
Ganin, Y.; Ustinova, E.; et al. 2016. Domain-adversarial training of neural networks. JMLR, 17(1): 2096-2030.
[9]
Gretton, A.; Borgwardt, K.; Rasch, M.; Scholkopf, B.; and Smola, A. 2006. A kernel method for the two-sample-problem. Advances in neural information processing systems, 19.
[10]
Haider, H.; Hoehn, B.; Davis, S.; and Greiner, R. 2020. Effective ways to build and evaluate individual survival distributions. Journal of Machine Learning Research, 21(85): 1-63.
[11]
Harrell, F. E.; Califf, R. M.; Pryor, D. B.; Lee, K. L.; and Rosati, R. A. 1982. Evaluating the yield of medical tests. Jama, 247(18): 2543-2546.
[12]
Harrell Jr, F. E.; Lee, K. L.; Califf, R. M.; Pryor, D. B.; and Rosati, R. A. 1984. Regression modelling strategies for improved prognostic prediction. Statistics in medicine, 3(2): 143-152.
[13]
Hoadley, K. A.; Yau, C.; Hinoue, T.; Wolf, D. M.; Lazar, A. J.; Drill, E.; Shen, R.; Taylor, A. M.; Cherniack, A. D.; Thorsson, V.; et al. 2018. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 173(2): 291-304.
[14]
Ishwaran, H.; and Kogalur, U. B. 2007. Random survival forests for R. R news, 7(2): 25-31.
[15]
Ishwaran, H.; Kogalur, U. B.; Blackstone, E. H.; and Lauer, M. S. 2008. Random survival forests. The annals of applied statistics, 2(3): 841-860.
[16]
Kaplan, E. L.; and Meier, P. 1958a. Nonparametric estimation from incomplete observations. Journal of the American statistical association, 53(282): 457-481.
[17]
Kaplan, E. L.; and Meier, P. 1958b. Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282): 457-481.
[18]
Katzman, J. L.; Shaham, U.; Cloninger, A.; Bates, J.; Jiang, T.; and Kluger, Y. 2018. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18(1): 1-12.
[19]
Kendall, M. G. 1948. Rank correlation methods. London, UK: Charles Griffin and Co. Ltd.
[20]
Khan, F. M.; and Zubek, V. B. 2008. Support vector regression for censored data (SVRc): a novel tool for survival analysis. In 2008 Eighth IEEE International Conference on Data Mining, 863-868. IEEE.
[21]
Krempl, G.; Žliobaite, I.; Brzeziński, D.; Hüllermeier, E.; Last, M.; Lemaire, V.; Noack, T.; Shaker, A.; Sievi, S.; Spiliopoulou, M.; et al. 2014. Open challenges for data stream mining research. ACM SIGKDD explorations newsletter, 16(1): 1-10.
[22]
Kuiper, N. H. 1960. Tests concerning random points on a circle. In Nederl. Akad. Wetensch. Proc. Ser. A, volume 63, 38-47.
[23]
Lee, C.; Zame, W.; Yoon, J.; and Van Der Schaar, M. 2018. Deephit: A deep learning approach to survival analysis with competing risks. In Proceedings of the AAAI conference on artificial intelligence, volume 32.
[24]
Lee, E. T. 1992. Statistical Methods for Survival Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2nd edition.
[25]
Li, Y.; Wang, J.; Ye, J.; and Reddy, C. K. 2016a. A multitask learning formulation for survival analysis. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1715-1724.
[26]
Li, Y.; Wang, L.; Wang, J.; Ye, J.; and Reddy, C. K. 2016b. Transfer learning for survival analysis via efficient l2, 1-norm regularized cox regression. In 2016 IEEE 16th International Conference on Data Mining (ICDM), 231-240. IEEE.
[27]
Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2008. Domain adaptation with multiple sources. Advances in neural information processing systems, 21.
[28]
Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2009. Domain adaptation: Learning bounds and algorithms. In 22nd Conference on Learning Theory, COLT 2009.
[29]
Mouli, S. C.; Teixeira, L.; Neville, J.; and Ribeiro, B. 2019. Deep lifetime clustering. arXiv preprint arXiv:1910.00547.
[30]
Nagpal, C.; Yadlowsky, S.; Rostamzadeh, N.; and Heller, K. 2021. Deep Cox mixtures for survival regression. In Machine Learning for Healthcare Conference, 674-708. PMLR.
[31]
Pei, Z.; Cao, Z.; Long, M.; and Wang, J. 2018. Multiadversarial domain adaptation. In AAAI, volume 32.
[32]
Richard, G.; de Mathelin, A.; Hebrail, G.; Mougeot, M.; and Vayatis, N. 2020. Unsupervised Multi-Source Domain Adaptation for Regression. In ECML.
[33]
Saito, K.; Kim, K.; et al. 2019. Semi-supervised domain adaptation via minimax entropy. In IEEE ICCV, 8050-8058.
[34]
Shaker, A.; and Hüllermeier, E. 2014. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1).
[35]
Shaker, A.; Yu, S.; and Onoro-Rubio, D. 2022. Learning to Transfer with von Neumann Conditional Divergence. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, 8231-8239.
[36]
Tibshirani, R. 1997. The lasso method for variable selection in the Cox model. Statistics in medicine, 16(4): 385-395.
[37]
Verweij, P. J.; and Van Houwelingen, H. C. 1994. Penalized likelihood in Cox regression. Statistics in medicine, 13(23-24): 2427-2436.
[38]
Wang, L.; Li, Y.; Zhou, J.; Zhu, D.; and Ye, J. 2017. Multitask survival analysis. In 2017 IEEE International Conference on Data Mining (ICDM), 485-494. IEEE.
[39]
Wang, P.; Li, Y.; and Reddy, C. K. 2019. Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR), 51(6): 1-36.
[40]
Yu, S.; Shaker, A.; Alesiani, F.; and Principe, J. C. 2020. Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications. In IJCAI, 2777-2784.
[41]
Zhao, H.; Zhang, S.; Wu, G.; Costeira, J. P.; Moura, J. M.; and Gordon, G. J. 2017. Multiple source domain adaptation with adversarial training of neural networks. arXiv preprint arXiv:1705.09684.
Recommendations
Nonparametric likelihood ratio goodness-of-fit tests for survival data
Berk and Jones (Z. Wahrsch. Verw. Gebiete 47 (1979) 47) described a nonparametric likelihood test of uniformity that is more efficient, in Bahadur's sense, than any weighted Kolmogorov-Smirnov test at any alternative. This article shows how to obtain a ...
Comments
Information & Contributors
Information
Published In
Copyright © 2023 Association for the Advancement of Artificial Intelligence.
Sponsors
- Association for the Advancement of Artificial Intelligence
Publisher
AAAI Press
Publication History
Published: 07 February 2023
Qualifiers
- Research-article
- Research
- Refereed limited
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 31 Dec 2024