Explainable AI for survival analysis: a median-SHAP approach
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929230726561792 |
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| author | Ter-Minassian, Lucile Ghalebikesabi, Sahra Diaz-Ordaz, Karla Holmes, Chris |
| author_facet | Ter-Minassian, Lucile Ghalebikesabi, Sahra Diaz-Ordaz, Karla Holmes, Chris |
| contents | With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define what we identify as an 'anchor point'. We show that the convention of using a mean anchor point may generate misleading interpretations for survival analysis and introduce median-SHAP, a method for explaining black-box models predicting individual survival times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable AI for survival analysis: a median-SHAP approach Ter-Minassian, Lucile Ghalebikesabi, Sahra Diaz-Ordaz, Karla Holmes, Chris Machine Learning Methodology With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define what we identify as an 'anchor point'. We show that the convention of using a mean anchor point may generate misleading interpretations for survival analysis and introduce median-SHAP, a method for explaining black-box models predicting individual survival times. |
| title | Explainable AI for survival analysis: a median-SHAP approach |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.00072 |