Summary of Explainable Ai For Survival Analysis: a Median-shap Approach, by Lucile Ter-minassian et al.
Explainable AI for survival analysis: a median-SHAP approach
by Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz, Chris Holmes
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); 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 This paper proposes a novel approach to explainable AI in medical applications. Specifically, it focuses on Shapley values, which have gained popularity for locally interpreting machine learning models. However, the authors highlight that the interpretation of these values strongly depends on two factors: the summary statistic and the estimator used. They introduce median-SHAP, a method designed to address this issue in survival analysis, where models predict individual patient outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is being used more often in medicine, but we need ways to understand how the computers make their decisions. One way is called Shapley values, which helps us see why a computer made a certain decision. But what if we’re trying to figure out why a model predicted someone’s chance of living or dying? We need a special kind of Shapley value just for that. This paper shows how to make those values work better in situations where time is important. |
Keywords
* Artificial intelligence * Machine learning
