A Novel Approach to Zero-Shot Drug-Drug Interaction Prediction Enabled by EHR-Augmented Knowledge Graphs

With more and more prescription drugs being administered, screening for adverse drug-drug interactions (DDIs) is now a major pharmacovigilance challenge. Electronic Health Record (EHR)-based statistical methods produce noisy predictions with high false positive rates due to confounding factors. Knowledge Graph (KG)-based machine learning methods, while more accurate, cannot predict interactions for drugs absent from the original graph, lacking zero-shot capability. We present a novel approach that augments large-scale but incomplete biomedical KGs with statistically noisy but comprehensive real-world edges derived from EHRs. We hypothesize that the EHR-derived associations act as bridges connecting unseen drugs to the pharmacological knowledge in KGs, thus enabling zero-shot capability. To rigorously test this, we designed a KG-embedding experiment that isolates drugs during training while preserving their interactions for testing. Results quantitatively demonstrate that our approach specifically enables effective zero-shot DDI prediction.