BRP-GABLE：一种面向多种结局类型的可解释基于规则的预测框架
BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types
- Wakayama Medical University（和歌山医科大学）
- Doshisha University（同志社大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
BRP-GABLE通过引导规则池化扩展GABLE，为多种结局类型构建稀疏可解释的预测模型，提升性能并保持临床可解释性。
AI中文摘要：
可解释的预测模型在生物医学研究中非常重要，因为在这些研究中，预测准确性通常需要与检查预测因子与结局之间关系的能力相平衡。自动二元逻辑估计（ABLE）通过使用阈值规则构建加性模型，提供了可解释的基于规则的表示。然而，原始方法仅限于二元结局，并且其贪婪前向构建遵循一条搜索路径，使得候选规则依赖于较早的选择，并可能排除替代结构。我们将ABLE扩展至二元、连续和时间至事件结局，使用针对结局特定的拟合标准，从而得到广义ABLE（GABLE）。随后，我们提出了用于GABLE的引导规则池化（BRP-GABLE），该方法将GABLE规则生成应用于引导样本，以探索替代搜索路径，汇集不同的规则，并在汇集规则和截断线性项上进行一次全局LASSO选择。与传统的装袋不同，引导特定模型不会被平均，重采样用于在构建最终模型之前使候选规则空间多样化。模拟显示，与GABLE相比，BRP-GABLE持续提高了预测性能，并以相对简单的规则结构提供了有竞争力的预测。应用于总生存期展示了其用于临床可解释预测的用途。BRP-GABLE提供了一个灵活的框架，用于构建跨多种结局类型的稀疏且可解释的预测模型。
英文摘要：
Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using threshold rules. However, the original method is restricted to binary outcomes, and its greedy forward construction follows a search path, making candidate rules dependent on earlier selections and potentially excluding alternative structures. We extended ABLE to binary, continuous, and time-to-event outcomes using outcome-specific fitting criteria, yielding Generalized ABLE (GABLE). We then propose bootstrap rule pooling for GABLE (BRP-GABLE), which applies GABLE rule generation to bootstrap samples to explore alternative search paths, pool distinct rules, and perform a single global LASSO selection over the pooled rules and truncated linear terms. Unlike conventional bagging, bootstrap-specific models are not averaged, and resampling is used to diversify the candidate rule space before constructing a final model. Simulations showed that BRP-GABLE consistently improved predictive performance compared with GABLE and provided competitive prediction with relatively simple rule structures. Application to overall survival demonstrated its use for clinically interpretable prediction. BRP-GABLE provides a flexible framework for constructing sparse and interpretable predictive models across multiple outcome types.
