Summary of Mensa: a Multi-event Network For Survival Analysis with Trajectory-based Likelihood Estimation, by Christian Marius Lillelund et al.
MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation
by Christian Marius Lillelund, Ali Hossein Gharari Foomani, Weijie Sun, Shi-ang Qi, Russell Greiner
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel deep learning approach, called MENSA, tackles the challenge of multi-event survival analysis by jointly learning representations of input features while capturing complex dependencies among events. Unlike existing approaches, MENSA optimizes a combination of traditional negative log-likelihood and trajectory-based likelihood to learn the temporal order of event occurrence. Empirical results on real-world clinical datasets demonstrate good discrimination performances and accurate time-to-event predictions in single-event, competing-risk, and multi-event problems. MENSA’s efficiency is another advantage, requiring fewer parameters and FLOPs compared to state-of-the-art survival baselines when applied to large-dimensional datasets (more than 100 features). This approach has the potential to improve risk assessment and disease diagnosis in various medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MENSA is a new way to predict when different things will happen. Imagine you’re trying to figure out how likely it is that someone will have a heart attack, get cancer, or die within a certain time frame. Most methods are simple and only give a risk score for each event, but MENSA tries to learn more about the events and how they relate to each other. It even looks at the order in which things happen over time. Tests on real medical data show that MENSA does a great job of predicting when these events will occur. Plus, it’s faster than some other methods. |
Keywords
» Artificial intelligence » Deep learning » Likelihood » Log likelihood
