在急性肺栓塞中检测右室功能障碍的临床决策支持系统:可解释的新二进制规则搜索 (BRS) 组合和稳定性评估
Mehmet Tahir Huyut1, Andrei Velichko2, Maksim Belyaev2
1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000, Erzincan, Turkey.
Artificial intelligence in medicine
|December 23, 2025
概括
二元规则搜索 (BRS) 提供了一种透明,可解释的方法,用于检测急性肺栓塞 (PE) 中的右心室功能障碍 (RVD). 这种方法为决策支持提供了临床上可接受的,保守的性能,特别是在资源有限的环境中.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在急性肺栓塞 (PE) 中,右室功能障碍 (RVD) 与短期死亡率的增加有关.
- 对于PE患者的RVD,急需快速和透明的风险分层工具.
研究的目的:
- 开发和验证一个可解释的二进制规则搜索 (BRS) 框架用于RVD检测.
- 在二元化临床数据集上评估框架的性能,强调保守的稳定性.
主要方法:
- 分析了一个单中心队列 (N=363),数据被二元化为0/1预测器.
- 基于Rust的BRS算法搜索了位掩码规则 (子集大小k=1-5) 以最大限度地提高开发数据上的马修斯相关系数 (MCC).
- 使用分层保持重复来评估性能,报告MCC,灵敏度,特异性,F1得分,AUC-ROC,以及稳定性限制规则得分 (SBRS) 的稳定性.
主要成果:
- 随着子集大小 (k) 的增加,规则性能通常会有所改善,三元组提供了节和强度的良好平衡.
- 在外部验证中,A k=4规则 (SexMale; ThromMain; Hypertense; HeartFail) 获得了0.791的灵敏度,0.557的特异性,F1的0.630和AUC-ROC的0.674.
- 血栓存在 (ThromMain/ThromBilat/DVT) 是一个关键预测因素,心力衰竭和高血压也对此有重大贡献.
结论:
- 可解释的BRS组件为RVD检测提供了临床上可接受和保守的性能.
- 该框架使用了很少的二进制预测器和透明的逻辑,适合临床医生面临的决策支持系统,特别是在资源有限的环境中.
- 建议对开发的BRS框架和相关的决策支持工具进行未来的多中心验证.
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