论文标题
survhap（t）：机器学习生存模型的时间依赖性解释
SurvSHAP(t): Time-dependent explanations of machine learning survival models
论文作者
论文摘要
与经典的统计学习方法相比，机器和深度学习生存模型表现出相似甚至改进的事件预测能力，但太复杂了，无法被人类解释。有几种模型不合时宜的解释可以克服这个问题。但是，没有一个直接解释生存函数预测。在本文中，我们介绍了Survhap（T），这是第一个允许解释生存黑盒模型的解释。它基于Shapley添加性解释，其理论基础稳定，并且在机器学习从业人员中广泛采用。所提出的方法旨在增强精确诊断和支持领域的专家做出决策。关于合成和医学数据的实验证实，survhap（t）可以检测具有时间依赖性效果的变量，并且其聚集是对变量对预测的重要性的决定性，而不是存活。 survhap（t）是模型不可替代的，可以应用于具有功能输出的所有模型。我们在http://github.com/mi2datalab/survshap中提供了与时间相关解释的可访问实现。
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.
