莫斯卡 - 针对心血管不良事件的多模式机会性查,并进行因果推理和消除混
Jialu Pi1, Juan Maria Farina2, Rimita Lahiri3
1SCAI, Arizona State University, Tempe, AZ, USA.
使用多式联络数据可以改进主要不良心血管事件 (MACE) 查. 我们的新型框架整合了胸部X射线 (CXR) 和心电图 (ECG),以更有效地识别有风险的个人.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 主要不良心血管事件 (MACE) 是全球主要的死亡原因.
- 使用常规健康数据的机会性查可以识别有风险的个人.
- 目前的风险评估模型由于采样偏差和单一模式限制而存在局限性.
研究的目的:
- 提出一个新的预测建模框架,MOSCARD,用于机会性心血管风险估计.
- 整合多式联络数据 (CXR和ECG) 与因果推理,以提高风险评估.
- 为了减轻机会性心血管风险预测中的偏见和混因素.
主要方法:
- 开发了MOSCARD,一个多式因果推理框架与共同注意.
- 实现了胸部X射线 (CXR) 与心电图 (ECG) 指导的多式调整.
- 使用双反向传播图来消除偏见.
主要成果:
- 与单一模式和最先进的模型相比,MOSCARD表现出更高的性能.
- 曲线下的实现面积 (AUC) 分数为0.75 (内部),0.83 (转移数据) 和0.71 (外部MIMIC).
- 该模型有效地调整了CXR和ECG数据,同时减轻了混因素.
结论:
- 拟议的MOSCARD框架为机会性心血管风险查提供了一种具有成本效益的方法.
- 整合多式联运数据和因果推理可以提高识别风险人群的准确性.
- 通过这种查进行的早期干预可以改善患者的治疗结果,并减少健康差异.
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