Explainable AI for survival analysis: a median-SHAP approach
Lucile Ter-Minassian Sahra Ghalebikesabi Karla Diaz-Ordaz Chris Holmes
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.