Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.

翻译：由于机器学习模型具有灵活性和优越性能，它们常常补充并超越传统的统计生存模型。然而，其广泛应用受到缺乏用户友好工具来解释内部运行机制和预测逻辑的限制。为解决这一问题，我们推出了survex R包，该包通过应用可解释人工智能技术，为解释任何生存模型提供了统一的框架。该软件的功能涵盖生存模型的理解与诊断，从而可推动其改进。通过揭示决策过程中的洞见（如变量效应与重要性），survex能够评估模型可靠性并检测偏差。因此，在生物医学研究和医疗保健等敏感领域，可促进透明性与责任性。