Changhee Lee (University of California), William R. Zame (University of California),
Jinsung Yoon (University of California), Mihaela van der Schaar (University of California, University of Oxford, Alan Turing Institute)
Overview
This paper proposes a deep neural network approach to survival analysis, to learn the distribution of survival times directly. The DeepHit model makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. The DeepHit model can handle competing risks; i.e. settings in which there is more than one possible event of interest.
Contributions and Distinctions from Previous Works
Methods
Survival analysis, neural networks.
Results
Cite
Changhee Lee, et, al. DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2020.
TL;DR
This paper proposes a deep neural network approach to survival analysis.
Paper Link
http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit
Author/Institution
Changhee Lee (University of California), William R. Zame (University of California),
Jinsung Yoon (University of California), Mihaela van der Schaar (University of California, University of Oxford, Alan Turing Institute)
Overview
This paper proposes a deep neural network approach to survival analysis, to learn the distribution of survival times directly. The DeepHit model makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. The DeepHit model can handle competing risks; i.e. settings in which there is more than one possible event of interest.
Contributions and Distinctions from Previous Works
Methods
Survival analysis, neural networks.
Results
Cite
Changhee Lee, et, al. DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2020.
Comments
Python code available: https://github.com/chl8856/DeepHit