HearteXplain:可解释的急性心力衰竭预测和血液生物标志物的识别使用EBMs和莫里斯灵敏度分析
Fatma Hilal Yagin1,2, Yasin Görmez3, Abdulmohsen Algarni4
1Department of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, Malatya, 44210, Turkey. hilal.yagin@gmail.com.
Scientific reports
|November 14, 2025
概括
这项研究引入了一个新的AI框架,使用可解释的人工智能 (XAI) 和莫里斯灵敏度分析 (MSA) 来诊断急性心力衰竭 (AHF). 像PDW和RDW-CV这样的血液学生物标志物显示出早期AHF检测的强大预测潜力.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
背景情况:
- 血液学生物标志物对于诊断急性心力衰竭 (AHF) 是至关重要的.
- 现有的AHF机器学习模型缺乏可解释性,阻碍了临床信任.
- 可解释的人工智能 (XAI) 和灵敏度分析为提高模型透明度提供了潜在的解决方案.
研究的目的:
- 开发和验证一个新的诊断框架,整合XAI和莫里斯灵敏度分析 (MSA) 以改善急性心力衰竭 (AHF) 检测.
- 提高机器学习模型的可解释性和性能,使用血液学生物标志物识别AHF.
- 确定具有高预测价值的关键血液学标志物,用于AHF诊断和风险分层.
主要方法:
- 对包括425名AHF患者和430名对照组在内的数据集的分析.
- 实施和评估八种机器学习模型,包括基于直方图的梯度提升 (histGB),XGBoost,可解释的提升机 (EBM) 和随机森林.
- 贝叶斯优化用于超参数调整和MSA用于特征重要性评估.
- 使用AUC,准确性,精度,回忆和Brier分数进行性能评估.
主要成果:
- 历史GB模型实现了最高的性能,曲线下的面积 (AUC) 为87.93%.
- MSA和EBM始终将血小板分布宽度 (PDW),红细胞分布宽度-CV (RDW-CV),中性粒细胞 (NEU),NEU/LY比率,年龄和白细胞计数 (WBC) 确定为最重要的预测特征.
- 已识别的血液学标志物显示出早期AHF诊断和风险分层的显著潜力.
结论:
- 结合XAI和MSA开发的框架为AHF预测提供了一种临床相关,可解释和具有成本效益的诊断策略.
- 通过XAI和MSA增强的模型透明度增加了临床信任,并促进了个性化治疗方法.
- 该框架确定了可访问的血液学生物标志物,可以显著改善AHF诊断和患者管理.
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