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

Srijith Chinthalapudi1, Sandeep K Mallipattu2, Alisa Yurovsky2

  • 1Edison Academy Magnet School, Edison, NJ.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 19, 2026
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Summary

This study introduces a new method to predict adverse drug-drug interactions (DDIs) by combining electronic health records (EHRs) with knowledge graphs (KGs). This approach enhances zero-shot DDI prediction for previously unseen drugs.

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Area of Science:

  • Pharmacovigilance and Biomedical Informatics

Background:

  • Screening for adverse drug-drug interactions (DDIs) is a critical pharmacovigilance task.
  • Electronic Health Record (EHR)-based statistical methods suffer from high false positive rates.
  • Knowledge Graph (KG)-based methods lack zero-shot prediction for new drugs.

Purpose of the Study:

  • To develop a novel approach for zero-shot DDI prediction.
  • To augment incomplete biomedical KGs with real-world EHR data.
  • To enable accurate prediction of DDIs for drugs not present in the initial KG.

Main Methods:

  • Augmenting large-scale biomedical KGs with EHR-derived edges.
  • Utilizing EHR associations as bridges to connect unseen drugs to KG knowledge.
  • Designing a KG-embedding experiment isolating drugs during training for rigorous testing.

Main Results:

  • The proposed method effectively enables zero-shot DDI prediction.
  • EHR-derived associations bridge the gap for unseen drugs in KGs.
  • Quantitative results demonstrate the specific efficacy of the approach for zero-shot DDI prediction.

Conclusions:

  • Combining EHR data with KGs overcomes limitations of individual methods.
  • The novel approach successfully achieves zero-shot DDI prediction capabilities.
  • This method represents a significant advancement in pharmacovigilance and drug safety.