在因果推理的MACE机会性查中减轻偏见
Jialu Pi1, Juan Maria Farina2, Chieh-Ju Chao3
1Department of Data Science & Eng, Arizona State University, 699 S Mill Ave BYENG, Suite 395, Tempe, 85281, Arizona, USA.
IEEE transactions on artificial intelligence
|January 7, 2026
概括
我们开发了一个因果推理框架,以减少人工智能临床工具的偏见,提高不同患者群体的准确性. 这种方法提高了人工智能的可靠性,以获得更好的医疗保健结果.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 因果推理因果推理
背景情况:
- 人口漂移在临床环境中显著影响AI模型的稳定性.
- 现有的偏差缓解策略往往忽视了慢性并发病的影响.
- 在不同的人口统计数据中准确的AI预测对于改善医疗保健至关重要.
研究的目的:
- 提出一种因果推理框架,解决人工智能模型中的选择偏差,以预测主要不良心血管事件 (MACE).
- 评估框架在不同患者群体和临床环境中的有效性.
- 提高人工智能驱动的临床决策的公平性和可靠性.
主要方法:
- 开发了一个因果推理框架,包括混者调整.
- 在高风险患者数据上训练了一个AI模型,并在低风险和外部数据集上评估它.
- 将因果框架与传统疾病分类,倾向性得分匹配和退化模型进行比较.
主要成果:
- 与基线模型相比,因果+混框架在转移和外部数据集上获得了较高的曲线下面面积 (AUC) 评分.
- 这种方法有效地减少了与混因素相关的差异.
- 在减轻选择偏差方面表现优于传统和最先进的退化方法.
结论:
- 整合因果推理和混调整可以提高AI模型在临床应用中的有效性.
- 拟议的框架显示了建立公平和强大的临床决策支持系统的希望.
- 这种方法通过考虑到人口变化,提高了人工智能在医疗保健中的可靠性和道德完整性.
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