تقرير
Explainable AI for survival analysis: a median-SHAP approach
| العنوان: | Explainable AI for survival analysis: a median-SHAP approach |
|---|---|
| المؤلفون: | Ter-Minassian, Lucile, Ghalebikesabi, Sahra, Diaz-Ordaz, Karla, Holmes, Chris |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning |
| الوصف: | 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. Comment: Accepted to the Interpretable Machine Learning for Healthcare (IMLH) workshop of the ICML 2022 Conference |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.00072 |
| رقم الأكسشن: | edsarx.2402.00072 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |