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

ABSTRACT: 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

SUBMITTER: He X 

PROVIDER: S-EPMC12780266 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature