With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the adoption of IML techniques promotes transparency, accountability and fairness in sensitive areas, such as clinical decision making processes, the development of targeted therapies, interventions or in other medical or healthcare related contexts. More specifically, explainability can uncover a survival model's potential biases and limitations and provide more mathematically sound ways to understand how and which features are influential for prediction or constitute risk factors. However, the lack of readily available IML methods may have deterred medical practitioners and policy makers in public health from leveraging the full potential of machine learning for predicting time-to-event data. We present a comprehensive review of the limited existing amount of work on IML methods for survival analysis within the context of the general IML taxonomy. In addition, we formally detail how commonly used IML methods, such as such as individual conditional expectation (ICE), partial dependence plots (PDP), accumulated local effects (ALE), different feature importance measures or Friedman's H-interaction statistics can be adapted to survival outcomes. An application of several IML methods to real data on data on under-5 year mortality of Ghanaian children from the Demographic and Health Surveys (DHS) Program serves as a tutorial or guide for researchers, on how to utilize the techniques in practice to facilitate understanding of model decisions or predictions.

翻译：随着黑箱机器学习模型的普及与快速发展，可解释机器学习（IML）或可解释人工智能（XAI）领域在过去十年中日益重要。这在生存分析中尤为突出：在临床决策、靶向疗法开发、干预措施制定及其他医疗健康相关敏感领域，采用IML技术有助于提升透明度、问责性和公平性。具体而言，可解释性能够揭示生存模型的潜在偏差与局限性，并提供更具数学严谨性的方法，以理解哪些特征影响预测或构成风险因素及其作用机制。然而，由于缺乏可直接使用的IML方法，可能阻碍医疗从业者和公共卫生政策制定者充分利用机器学习预测时间-事件数据的潜力。本文基于通用IML分类框架，对生存分析领域现有有限数量的IML方法研究进行了全面综述。此外，我们详细阐述了如何将个体条件期望（ICE）、部分依赖图（PDP）、累积局部效应（ALE）、多种特征重要性指标及Friedman H交互统计量等常用IML方法适配至生存结果。通过将多种IML方法应用于人口与健康调查（DHS）项目中加纳儿童五岁以下死亡率真实数据的案例研究，为研究者提供了实践指南，以促进对模型决策或预测的理解。