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

Authors

  • Changhee Lee University of California, Los Angeles
  • William Zame University of California, Los Angeles
  • Jinsung Yoon University of California, Los Angeles
  • Mihaela van der Schaar University of Oxford; University of California, Los Angeles; Alan Turing Institute

DOI:

https://doi.org/10.1609/aaai.v32i1.11842

Keywords:

survival analysis, competing risks, neural networks, first-hitting-time analysis

Abstract

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.

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Published

2018-04-26

How to Cite

Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11842

Issue

Section

Main Track: Machine Learning Applications