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可解释图形卷积网络用于预测2型糖尿病患者心血管疾病风险

Ioannis Siachos¹, Maria Athanasiou¹, Konstantia Zarkogianni¹

¹School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.

Journal of biomedical informatics

|March 14, 2026

概括

本研究引入了一个可解释的图形神经网络 (GNN),用于预测2型糖尿病患者的心血管疾病 (CVD) 风险,实现高准确性并为改善患者护理提供临床相关解释.

科学领域:

人工智能在医学中的应用
心血管健康心血管健康
糖尿病管理糖尿病管理

背景情况:

心血管疾病 (CVD) 是2型糖尿病患者 (T2DM) 死亡的主要原因.
传统的风险计算器在捕捉复杂的风险因素相互作用方面存在局限性.
图形神经网络 (GNN) 为复杂的关系建模提供了潜力,但往往缺乏可解释性.

研究的目的:

在T2DM中开发第一个可解释的GNN框架,用于CVD风险预测.
将GNN与基于规则的替代模型集成为透明的临床AI.
提高T2DM患者心血管疾病风险评估的准确性和可解释性.

主要方法:

从560名T2DM患者的人口统计,生活方式和临床数据构建了一个人口图.
采用GNN分类器使用基于图形的异常检测损失函数来处理类不平衡.
使用RuleFit替代模型进行后期解释和提取基于规则的全局解释.

主要成果:

实现了0.786±0.076的曲线下的面积 (AUC),超过了基准方法.
证明了精确校准的预测,布赖尔得分为0.053±0.021.
规则Fit的解释确定了已知的心血管疾病风险因素和新的中间风险模式,例如残留性脂质失调症.

结论:

可解释的GNN框架为T2DM患者提供可靠的心血管疾病风险估计.
该模型提供了透明和临床相关的解释,增强了信任和理解.
集成到T2DM临床查工具的潜力,改善心血管疾病风险管理.

关键词:

异常检测检测异常检测心血管疾病是什么心血管疾病可解释的人工智能全球代孕模型的全球代孕模型图形神经网络的神经网络机器学习是机器学习.2型糖尿病糖尿病类型 2型糖尿病

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