University of Cambridge > > MRC Biostatistics Unit Seminars > BSU Seminar: "Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis"

BSU Seminar: "Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis"

Add to your list(s) Download to your calendar using vCal

  • UserProf Christopher Yau, University of Oxford
  • ClockThursday 10 November 2022, 14:00-15:00
  • HouseVirtual Seminar .

If you have a question about this talk, please contact Alison Quenault.

This will be a free online seminar. To register to attend virtually, please click here:

Survival models — particularly those able to account for patient comorbidities via competing risks analysis — offer valuable prognostic information to clinicians making critical decisions and represent a growing area of application for machine learning approaches. However, current methods typically involve restrictive parameterisations, discretisation of time or the modelling of only one event cause. In this talk, I highlight how general cumulative distribution functions can be naturally expressed via neural network-based ordinary differential equations and how this can be utilised in survival analysis. In particular, we present DeSurv, a neural derivative-based approach capable of avoiding the aforementioned restrictions and flexibly modelling competing-risk survival data in continuous time. We apply DeSurv to both single-risk and competing-risk synthetic and real-world datasets and obtain results which compare favourably with current state-of-the-art models.

This talk is part of the MRC Biostatistics Unit Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity