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Computer-Interpretable Domain Knowledge for Drug-Induced Acute Kidney Injury: a Knowledge Graph Approach
Romy Vos1, Iacopo Vagliano2,3, Cornelis Boersma4,5,6
1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. r.m.p.vos@student.vu.nl.
Scientific Data
|June 10, 2026
Summary
A new Knowledge Graph (KG) for Drug-Induced Acute Kidney Injury (DAKI) organizes drug information to aid in identifying kidney risks. This DAKI KG supports clinical decision-making and machine learning for better patient outcomes.
Area of Science:
- Pharmacology
- Biomedical Informatics
- Nephrology
Background:
- Drug-induced acute kidney injury (AKI) poses a significant clinical challenge.
- Existing knowledge on DAKI is often fragmented across various sources.
- Standardized data representation is crucial for effective analysis and clinical support.
Purpose of the Study:
- To develop a comprehensive Knowledge Graph (KG) for Drug-Induced Acute Kidney Injury (DAKI).
- To integrate and standardize knowledge from diverse, credible sources.
- To demonstrate the KG's utility in clinical decision support and research.
Main Methods:
- Integrated data from multiple domain-specific sources.
- Standardized information to international vocabularies.
- Validated the DAKI KG using competency questions and expert evaluation.
Main Results:
- The DAKI KG successfully integrates and standardizes information on drugs causing AKI.
- Demonstrated applicability through competency questions and expert reviews.
- Identified potential for decision support tools and machine learning model inputs.
Conclusions:
- The developed DAKI KG provides a structured knowledge base for drug-induced kidney injury.
- The KG shows promise for identifying side effects, drug-drug interactions, and aiding research.
- Further refinement is necessary for direct clinical application, but it is valuable for confounder identification and QA systems.
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