标题： 可解释的人工智能用于生存分析：中位数-SHAP方法 显示英文标题

标题： Explainable AI for survival analysis: a median-SHAP approach

摘要： 随着机器学习被纳入常规临床实践，需要针对医疗应用的可解释人工智能方法。 Shapley 值在局部解释模型方面引起了广泛关注。 在这里，我们证明其解释强烈依赖于摘要统计量及其估计器，而这又定义了我们所确定的“锚点”。 我们表明，使用均值锚点的惯例可能在生存分析中产生误导性解释，并引入中位数 SHAP，这是一种用于解释预测个体生存时间的黑盒模型的方法。 显示英文摘要

摘要： 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.