提升生存分析树的可视化与可解释性，让决策过程更透明。
Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
- 通过新方法增强功能生存树的可读性
- 在真实与模拟数据上准确捕捉决策逻辑
- 适合医疗风险评估等需透明决策的场景
功能生存模型是分析具有复杂预测因子（如函数型或高维输入）的时间-事件数据的关键工具。尽管其预测能力较强，但通常缺乏可解释性，限制了其在实际决策和风险分析中的应用。本研究聚焦两种关键生存模型：功能生存树（FST）和功能随机生存森林（FRSF）。提出新方法与工具，以增强FST模型的可解释性，并提升FRSF集成模型的可解释性。基于真实与模拟数据集的实验表明，所提方法能生成高效、易理解的决策树，准确反映模型集成的内在决策过程。
原文摘要 · Abstract (English)
Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the Functional Survival Tree (FST) and the Functional Random Survival Forest (FRSF). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Using both real and simulated datasets, the results demonstrate that the proposed approaches yield efficient, easy-to-understand decision trees that accurately capture the underlying decision-making processes of the model ensemble.
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