Summary of Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks, by Julie Alberge (soda) et al.
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
by Julie Alberge, Vincent Maladière, Olivier Grisel, Judith Abécassis, Gaël Varoquaux
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed SurvivalBoost method addresses challenges in predicting the most likely event among multiple classes of outcomes when dealing with right-censored data. The approach utilizes a strictly proper censoring-adjusted separable scoring rule, enabling optimization on a subset of the data and addressing limitations of classic competing risks models. SurvivalBoost outperforms 12 state-of-the-art models across several metrics on four real-life datasets, offering great calibration, the ability to predict across any time horizon, and computation times faster than existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Survival analysis is used to study when an event will happen. When there are multiple events, it’s called competing risks. Classic methods don’t work well with this type of data. The new SurvivalBoost method solves these problems by designing a special way to calculate the probability of each event and optimize the model using a subset of the data. This approach is better than 12 other state-of-the-art models on four real-life datasets, making it more accurate and efficient. |
Keywords
» Artificial intelligence » Optimization » Probability
