BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types
BRP-GABLE：适用于多种结果类型的可解释规则预测框架AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：文本研究生物医学中的通用规则预测与生存预测，未提供所列经济或金融领域细分的直接依据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
BRP-GABLE：适用于多种结果类型的可解释规则预测框架
- 研究问题
- 如何将可解释的阈值规则预测扩展至二元、连续及生存结果，并缓解贪心规则搜索受早期选择限制的问题？
- 方法与思路
- 作者先构建 GABLE，再在自助样本中生成并汇集候选规则，对规则及截断线性项统一进行 LASSO 选择形成最终模型。
- 材料陈述的贡献
- 摘要称该方法通过重采样扩展候选规则空间，在模拟中改善相对 GABLE 的预测表现，并用于可解释的总体生存预测。
- 阅读关注点
- 应核对各结果类型的拟合准则、自助抽样与规则去重方式、LASSO 调参流程，以及预测评估是否使用独立验证数据。
- 阅读边界
- 这是 arXiv 预印本，不能据此声称已经同行评审或期刊接收；摘要未提供改善幅度，规则关系也不能直接解释为因果效应。
原文摘要
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.