ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2409.06525
64
1
v1v2v3 (latest)

MENSA: A Multi-Event Network for Survival Analysis under Informative Censoring

10 September 2024
Christian Marius Lillelund
Ali Hossein Gharari Foomani
Weijie Sun
Shi-ang Qi
Russell Greiner
ArXiv (abs)PDFHTML
Main:40 Pages
14 Figures
Bibliography:5 Pages
14 Tables
Abstract

Given an instance, a multi-event survival model predicts the time until that instance experiences each of several different events. These events are not mutually exclusive and there are often statistical dependencies between them. There are relatively few multi-event survival results, most focusing on producing a simple risk score, rather than the time-to-event itself. To overcome these issues, we introduce MENSA, a novel, deep learning approach for multi-event survival analysis that can jointly learn representations of the input covariates and the dependence structure between events. As a practical motivation for multi-event survival analysis, we consider the problem of predicting the time until a patient with amyotrophic lateral sclerosis (ALS) loses various physical functions, i.e., the ability to speak, swallow, write, or walk. When estimating when a patient is no longer able to swallow, our approach achieves an L1-Margin loss of 278.8 days, compared to 355.2 days when modeling each event separately. In addition, we also evaluate our approach in single-event and competing risk scenarios by modeling the censoring and event distributions as equal contributing factors in the optimization process, and show that our approach performs well across multiple benchmark datasets. The source code is available at: https://github.com/thecml/mensa

View on arXiv
@article{lillelund2025_2409.06525,
  title={ MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation },
  author={ Christian Marius Lillelund and Ali Hossein Gharari Foomani and Weijie Sun and Shi-ang Qi and Russell Greiner },
  journal={arXiv preprint arXiv:2409.06525},
  year={ 2025 }
}
Comments on this paper