Survival analysisDeep Learning
DeepSurv
DeepSurv is a deep neural network approach to survival analysis that learns personalized survival distributions directly from data. Introduced by Katzman et al. in 2018, it extends the Cox proportional hazards model using deep learning to capture complex, nonlinear relationships between covariates and survival outcomes. It solves the problem of modeling heterogeneous treatment effects and time-to-event predictions in high-dimensional settings.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82. DOI: 10.1002/sim.4780140108 ↗
- Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51. DOI: 10.48550/arXiv.1606.00931 ↗
- Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). Deephit: A deep learning approach for dynamic survival analysis. AAAI Conference on Artificial Intelligence, 32(1). link ↗