Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction

TL;DR

This paper introduces a hybrid method combining knowledge graphs and Bayesian networks to improve explainability and accuracy in disease risk prediction using multimodal EHR data, addressing uncertainty and personalization.

Contribution

It presents a novel approach for constructing Bayesian networks from ontology-based knowledge graphs and EHR data, enhancing explainability and handling uncertainty in disease risk prediction.

Findings

Effective balance of general medical knowledge and patient-specific data

Handles uncertainty in incomplete EHR data

Achieves good predictive performance in atrial fibrillation prediction

Abstract

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.