用知识图谱与贝叶斯网络融合，实现可解释的疾病风险预测
Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
- 从医学知识图谱和电子病历构建贝叶斯网络
- 在房颤预测中表现良好且能处理数据不确定性
- 适合需要透明决策的临床辅助系统使用
多模态电子健康记录（EHR）数据结合医学领域知识可用于疾病风险预测。然而，通用医学知识需适配具体医疗环境和患者群体才能实际应用。同时，风险预测系统须在数据不完整、健康结果非确定性的条件下处理不确定性，并保持可解释性。通过整合知识图谱（KGs）与贝叶斯网络（BNs），可缓解上述挑战。本文提出一种从基于本体的知识图谱和多模态EHR数据构建贝叶斯网络的新方法，用于可解释的疾病风险预测。以心房颤动为例，基于真实EHR数据的应用案例表明，该方法在兼顾通用医学知识与患者特异性背景的基础上，有效处理不确定性，具备高度可解释性，并取得良好预测性能。
原文摘要 · Abstract (English)
Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient populations to achieve practical clinical use. Additionally, risk prediction systems must handle uncertainty from incomplete data and non-deterministic health outcomes while remaining explainable. These challenges can be alleviated by the integration of knowledge graphs (KGs) and Bayesian networks (BNs). We present a novel approach for constructing BNs from ontology-based KGs and multimodal EHR data for explainable disease risk prediction. Through an application use case of atrial fibrillation and real-world EHR data, we demonstrate that the approach balances generalised medical knowledge with patient-specific context, effectively handles uncertainty, is highly explainable, and achieves good predictive performance.
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