Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explain the survival function prediction. In this paper, we introduce SurvSHAP(t), the first time-dependent explanation that allows for interpreting survival black-box models. It is based on SHapley Additive exPlanations with solid theoretical foundations and a broad adoption among machine learning practitioners. The proposed methods aim to enhance precision diagnostics and support domain experts in making decisions. Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME. SurvSHAP(t) is model-agnostic and can be applied to all models with functional output. We provide an accessible implementation of time-dependent explanations in Python at http://github.com/MI2DataLab/survshap .

本文介绍一种基于SHapley加性解释的SurvSHAP(t)方法，使用其可以对时间相关的生存黑盒模型进行解释，提高精度诊断，支持领域专家决策。实验结果表明，SurvSHAP(t)对时间相关变量的影响检测更准确，其聚合也比SurvLIME更好地确定了预测的变量重要性。

SurvSHAP(t): 机器学习生存模型的时间依赖性解释