Summary of Toward Conditional Distribution Calibration in Survival Prediction, by Shi-ang Qi et al.
Toward Conditional Distribution Calibration in Survival Prediction
by Shi-ang Qi, Yakun Yu, Russell Greiner
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 method, based on conformal prediction, improves survival prediction by enhancing individual decision-making through conditional calibration. The approach uses a model’s predicted individual survival probability at an instance’s observed time to effectively improve marginal and conditional calibration without compromising discrimination. Asymptotic theoretical guarantees are provided for both marginal and conditional calibration, and the method is tested extensively across 15 diverse real-world datasets, demonstrating its practical effectiveness and versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how long people will live. Current methods focus on being accurate, but this approach focuses on making sure the predictions are right for each individual person. The method uses a model’s prediction of how likely someone is to survive at a certain point in time. This helps improve the model’s overall accuracy and makes it better for real-world decisions. |
Keywords
» Artificial intelligence » Probability
