MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation

Christian Marius Lillelund, Ali Hossein Gharari Foomani, Weijie Sun, Shi-ang Qi, Russell Greiner,  The Pooled Resource Open-Access ALS Clinical Trials Consortium (PRO-ACT)
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:543-571, 2026.

Abstract

Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing. A common workaround is to train independent single-event models for such multi-event problems, but this approach fails to exploit dependencies and shared structures across events. To overcome these limitations, we propose {MENSA} (Multi-Event Network for Survival Analysis), a deep learning model that jointly learns flexible time-to-event distributions for multiple events, whether competing or co-occurring. In addition, we introduce a novel trajectory-based likelihood term that captures the temporal ordering between events. Across four multi-event datasets, {MENSA} improves predictive performance over many state-of-the-art baselines. Source code is available at https://github.com/thecml/mensa.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-lillelund26a, title = {{MENSA}: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation}, author = {Lillelund, Christian Marius and Gharari Foomani, Ali Hossein and Sun, Weijie and Qi, Shi-ang and Greiner, Russell and {The Pooled Resource Open-Access ALS Clinical Trials Consortium ({PRO-ACT})}}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {543--571}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/lillelund26a/lillelund26a.pdf}, url = {https://proceedings.mlr.press/v297/lillelund26a.html}, abstract = {Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing. A common workaround is to train independent single-event models for such multi-event problems, but this approach fails to exploit dependencies and shared structures across events. To overcome these limitations, we propose {MENSA} (Multi-Event Network for Survival Analysis), a deep learning model that jointly learns flexible time-to-event distributions for multiple events, whether competing or co-occurring. In addition, we introduce a novel trajectory-based likelihood term that captures the temporal ordering between events. Across four multi-event datasets, {MENSA} improves predictive performance over many state-of-the-art baselines. Source code is available at https://github.com/thecml/mensa.} }
Endnote
%0 Conference Paper %T MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation %A Christian Marius Lillelund %A Ali Hossein Gharari Foomani %A Weijie Sun %A Shi-ang Qi %A Russell Greiner %A The Pooled Resource Open-Access ALS Clinical Trials Consortium (PRO-ACT) %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-lillelund26a %I PMLR %P 543--571 %U https://proceedings.mlr.press/v297/lillelund26a.html %V 297 %X Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing. A common workaround is to train independent single-event models for such multi-event problems, but this approach fails to exploit dependencies and shared structures across events. To overcome these limitations, we propose {MENSA} (Multi-Event Network for Survival Analysis), a deep learning model that jointly learns flexible time-to-event distributions for multiple events, whether competing or co-occurring. In addition, we introduce a novel trajectory-based likelihood term that captures the temporal ordering between events. Across four multi-event datasets, {MENSA} improves predictive performance over many state-of-the-art baselines. Source code is available at https://github.com/thecml/mensa.
APA
Lillelund, C.M., Gharari Foomani, A.H., Sun, W., Qi, S., Greiner, R. & The Pooled Resource Open-Access ALS Clinical Trials Consortium (PRO-ACT), . (2026). MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:543-571 Available from https://proceedings.mlr.press/v297/lillelund26a.html.

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