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.

翻译：随着机器学习在常规临床实践中的应用，对专门针对医疗领域的可解释人工智能方法的需求日益增长。Shapley值在局部解释模型方面引起了广泛兴趣。在此，我们证明了其解释在很大程度上既依赖于汇总统计量又依赖于该统计量的估计量，而这些反过来又定义了我们所谓的“锚点”。我们表明，使用均值锚点的惯例可能会为生存分析生成误导性解释，并引入中位数-SHAP（median-SHAP），这是一种解释预测个体生存时间的黑箱模型的方法。