MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911275601100800 |
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| author | Lillelund, Christian Marius Foomani, Ali Hossein Gharari Sun, Weijie Qi, Shi-ang Greiner, Russell |
| author_facet | Lillelund, Christian Marius Foomani, Ali Hossein Gharari Sun, Weijie Qi, Shi-ang Greiner, Russell |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation Lillelund, Christian Marius Foomani, Ali Hossein Gharari Sun, Weijie Qi, Shi-ang Greiner, Russell Machine Learning 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. |
| title | MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.06525 |