用谢尔比值生成可解释的点数风险评分，无需人工预处理。
SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values
- 基于梯度提升树与谢尔比值结合，实现任务无关的风险评分
- 在多种任务中表现媲美顶尖方法，速度却快得多
- 适合需要透明、高效风险分层的医疗决策场景
可解释的风险评分在临床决策支持中至关重要，但传统方法常依赖人工预处理、特定任务建模和简化假设，限制了灵活性与预测能力。我们提出 SHAPoint，一种新型、任务无关的框架，将梯度提升树的预测准确性与点数风险评分的可解释性相结合。SHAPoint 支持分类、回归和生存分析任务，并继承树模型的特性，如原生处理缺失数据和单调性约束。相比现有框架，SHAPoint 具有更高灵活性，减少对人工预处理的依赖，且运行速度更快。实验表明，SHAPoint 生成的评分紧凑且可解释，预测性能接近当前最优方法，但运行时间仅为几分之一，是透明且可扩展的风险分层有力工具。
原文摘要 · Abstract (English)
Interpretable risk scores play a vital role in clinical decision support, yet traditional methods for deriving such scores often rely on manual preprocessing, task-specific modeling, and simplified assumptions that limit their flexibility and predictive power. We present SHAPoint, a novel, task-agnostic framework that integrates the predictive accuracy of gradient boosted trees with the interpretability of point-based risk scores. SHAPoint supports classification, regression, and survival tasks, while also inheriting valuable properties from tree-based models, such as native handling of missing data and support for monotonic constraints. Compared to existing frameworks, SHAPoint offers superior flexibility, reduced reliance on manual preprocessing, and faster runtime performance. Empirical results show that SHAPoint produces compact and interpretable scores with predictive performance comparable to state-of-the-art methods, but at a fraction of the runtime, making it a powerful tool for transparent and scalable risk stratification.
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