DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks

By Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar. AAAI Conference on Artificial Intelligence, 2018

819 citations recorded by the source corpus.

Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times(times-to-event). Much of the previous work has approached the problem by viewing the survival time as the first hitting time of a stochastic process, assuming a specific form for the underlying stochastic process, using available data to learn the relationship between the covariates and the parameters of the model, and then deducing the relationship between covariates and the distribution of first hitting times (the risk). However, previous models rely on strong parametric assumptions that are often violated. This paper proposes a very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly.DeepHit makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. Most importantly, DeepHit smoothly handles competing risks; i.e. settings in which there is more than one possible event of interest.Comparisons with previous models on the basis of real and synthetic datasets demonstrate that DeepHit achieves large and statistically significant performance improvements over previous state-of-the-art methods.

Subfields: Demography, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/803a7b26bdc0feafbf45bc5d57c2bc3f55b6f8fc

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In Demography

Mihaela van der Schaar

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018, background): “, 2017) survival analysis with competing risks (Lee et al., 2018; Alaa and van der Schaar, 2017) and treatment recommendations (Katzman et al.”
Time-to-Event Prediction with Neural Networks and Cox Regression (2019, methodology): “Model hyperparameter tuning for DeepHit and RSF was performed with the time-dependent concordance index (Antolini et al., 2005).”