Survival Mixture Density Networks

Xintian Han, Mark Goldstein, Rajesh Ranganath

Research output: Contribution to journalConference articlepeer-review


Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been proposed for survival analysis. However, the training of neural ODEs is slow due to the high computational complexity of neural ODE solvers. Here, we propose an efficient alternative for flexible continuous time models, called Survival Mixture Density Networks (Survival MDNs). Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs). While MDNs produce flexible real-valued distributions, the invertible positive function maps the model into the time-domain while preserving a tractable density. Using four datasets, we show that Survival MDN performs better than, or similarly to continuous and discrete time baselines on concordance, integrated Brier score and integrated binomial log-likelihood. Meanwhile, Survival MDNs are also faster than ODE-based models and circumvent binning issues in discrete models.

Original languageEnglish (US)
Pages (from-to)224-248
Number of pages25
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event7th Machine Learning for Healthcare Conference, MLHC 2022 - Durham, United States
Duration: Aug 5 2022Aug 6 2022

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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