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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
Open Access

LEVERAGING DEEP LEARNING IN SURVIVAL ANALYSIS FOR ENHANCED TIME-TO-EVENT PREDICTION

DOI https://doi.org/10.55640/ijaair-v02i05-01
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Dr. Kenji Yamamoto 1 , Prof. Lijuan Wang 1 ,
4 Department of Artificial Intelligence, University of Tokyo, Japan
4 School of Data Science, Fudan University, China

Abstract

Survival analysis is a critical statistical approach for modeling time-to-event outcomes across disciplines such as healthcare, engineering, and social sciences. Traditional methods, including Cox proportional hazards models, often struggle with complex, high-dimensional data and non-linear relationships. Recent advancements in deep learning have led to innovative models that significantly enhance survival prediction by capturing intricate patterns and dependencies in time-to-event data. This study explores state-of-the-art deep learning frameworks—such as DeepSurv, DeepHit, and recurrent neural networks—for survival analysis, emphasizing their architecture, performance, and application across diverse datasets. The integration of deep learning enables more accurate risk estimation and personalized prognostics, revolutionizing predictive modeling in survival data contexts. We also discuss challenges related to interpretability, data censoring, and model evaluation, proposing future research directions for robust and explainable deep survival models.

Keywords

Deep learning, survival analysis, time-to-event prediction, neural networks

References

Aalen, O. O. and S. Johansen. (1978). “An empirical transition matrix for non-homogeneous Markov chains based on censored observations”. Scandinavian Journal of Statistics: 141–150.
Aalen, O. O. (1978). “Nonparametric inference for a family of counting processes”. The Annals of Statistics. 6(4): 701–726.
Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. url: https://www.tensorflow.org/.
Allison, P. D. (1982). “Discrete-time methods for the analysis of event histories”. Sociological Methodology. 13: 61–98.
Angelopoulos, A. N. and S. Bates. (2023). “A gentle introduction to conformal prediction and distribution-free uncertainty quantification”. Foundations and Trends¼ in Machine Learning. 16(4): 494–591.
Antolini, L., P. Boracchi, and E. Biganzoli. (2005). “A time-dependent discrimination index for survival data”. Statistics in Medicine. 24(24): 3927–3944.
Avati, A., T. Duan, S. Zhou, K. Jung, N. H. Shah, and A. Y. Ng. (2020). “Countdown regression: Sharp and calibrated survival predictions”. In: Uncertainty in Artificial Intelligence. PMLR. 145–155.
Bacaër, N. (2011). A Short History of Mathematical Population Dynamics. Vol. 618. Springer.
Beran, R. (1981). “Nonparametric regression with randomly censored survival data”. Technical report, University of California, Berkeley.
Bertsimas, D., J. Dunn, E. Gibson, and A. Orfanoudaki. (2022). “Optimal survival trees”. Machine Learning. 111(8): 2951–3023.
Blanche, P., M. W. Kattan, and T. A. Gerds. (2019). “The c-index is not proper for the evaluation of-year predicted risks”. Biostatistics. 20(2): 347–357.
Blanche, P., A. Latouche, and V. Viallon. (2013). “Time-dependent AUC with right-censored data: a survey”. Risk Assessment and Evaluation of Predictions: 239–251.
Blei, D. M., A. Kucukelbir, and J. D. McAuliffe. (2017). “Variational inference: A review for statisticians”. Journal of the American Statistical Association. 112(518): 859–877.
Boag, J. W. (1949). “Maximum likelihood estimates of the proportion of patients cured by cancer therapy”. Journal of the Royal Statistical Society Series B: Statistical Methodology. 11(1): 15–53.
Böhmer, P. E. (1912). “Theorie der unabhĂ€ngigenWahrscheinlichkeiten”. In: Rapports Memoires et Proces verbaux de Septieme Congres International dActuaires Amsterdam. Vol. 2. 327–343.
Bommasani, R. et al. (2021). “On the opportunities and risks of foundation models”. arXiv preprint arXiv:2108.07258.
Box-Steffensmeier, J. M. and B. S. Jones. (2004). Event History Modeling: A Guide for Social Scientists. Cambridge University Press.
Bradbury, J., R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang. (2018). JAX: composable transformations of Python+NumPy programs. Version 0.3.13. url: http://github.com/jax-ml/jax.
Breslow, N. (1972). Discussion of the paper by D R Cox (1972). Journal of the Royal Statistical Society, Series B. 34(2):216–217.
Brown, C. C. (1975). “On the use of indicator variables for studying the time-dependence of parameters in a response-time model”. Biometrics. 31(4): 863–872.
Buolamwini, J. and T. Gebru. (2018). “Gender shades: Intersectional accuracy disparities in commercial gender classification”. In: Conference on Fairness, Accountability and Transparency. PMLR. 77–91.
Candùs, E., L. Lei, and Z. Ren. (2023). “Conformalized survival analysis”. Journal of the Royal Statistical Society Series B: Statistical Methodology. 85(1): 24–45.
Chagny, G. and A. Roche. (2014). “Adaptive and minimax estimation of the cumulative distribution function given a functional covariate”. Electronic Journal of Statistics. 8(2): 2352–2404.
Chapelle, O. (2014). “Modeling delayed feedback in display advertising”. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1097–1105.
Chapfuwa, P., S. Assaad, S. Zeng, M. J. Pencina, L. Carin, and R. Henao. (2021). “Enabling counterfactual survival analysis with balanced representations”. In: Proceedings of the Conference on Health, Inference, and Learning. 133–145.
Chapfuwa, P., C. Li, N. Mehta, L. Carin, and R. Henao. (2020). “Survival cluster analysis”. In: Proceedings of the ACM Conference on Health, Inference, and Learning. 60–68.
Chapfuwa, P., C. Tao, C. Li, C. Page, B. Goldstein, L. C. Duke, and R. Henao. (2018). “Adversarial time-to-event modeling”. In: International Conference on Machine Learning. PMLR. 735–744.
Chen, G. H. (2019). “Nearest neighbor and kernel survival analysis: Nonasymptotic error bounds and strong consistency rates”. In: International Conference on Machine Learning. PMLR. 1001–1010.
Chen, G. H. (2020). “Deep kernel survival analysis and subject-specific survival time prediction intervals”. In: Machine Learning for Healthcare Conference. PMLR. 537–565.
Chen, G. H. (2023). “A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information”. In: Conference on Health, Inference, and Learning. PMLR. 440–476.
Harrell, F. E., R. M. Califf, D. B. Pryor, K. L. L. D. Rosati, and R. A. Rosati. (1982). “Evaluating the yield of medical tests”. Journal of the American Medical Association. 247(17): 2543–2546.
Kaplan, E. L. and P. Meier. (1958). “Nonparametric estimation from incomplete observations”. Journal of the American Statistical Association. 53(282): 457–481.
Dip Bharatbhai Patel. (2025). Leveraging BI for Competitive Advantage: Case Studies from Tech Giants. Frontiers in Emerging Engineering & Technologies, 2(04), 15–21. Retrieved from https://irjernet.com/index.php/feet/article/view/166

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