Heterogeneous graph neural network-based prediction of immune-related adverse events

Immune-related adverse events (irAEs) are common and potentially fatal adverse events. However, predicting irAEs based on clinical medication regimens and basic patient information remains a significant clinical challenge. This study aims to develop a prediction model using graph neural networks with electronic health records (EHRs), thereby reducing irAEs risk. Our method based on heterogeneous graph networks. It incorporates medications, diagnoses and patients characteristics from EHRs as nodes to predict irAEs occurrence. Medication-policy simulation, case studies and interpretability analyses were conducted to align the model with real-world clinical needs. Compared to other baseline methods, our method shows superior performance across all evaluation metrics: with AUC of 0.902, AUPRC of 0.85, precision of 0.709, RECALL of 0.799, F1 score of 0.751, accuracy of 0.851. About simulation study, the model demonstrated progressive improvement, reflected in a 5%-6% increase across six evaluation metrics. Interpretability analysis revealed that distinct risk patterns emerge at different treatment stages. Our approach exhibits robust reliability and outperforms other methods for irAEs prediction. Our study further establishes a novel paradigm for personalized therapy monitoring and early intervention. This methodology holds potential for reducing irAEs risk.