ArticleScientific data2026
Computer-Interpretable Domain Knowledge for Drug-Induced Acute Kidney Injury: a Knowledge Graph Approach.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Computer-Interpretable Domain Knowledge for Drug-Induced Acute Kidney Injury: a Knowledge Graph Approach.Scientific data · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In this study, a Knowledge Graph (KG) for Drug-Induced Acute Kidney Injury (DAKI) was developed to provide structured and standardized knowledge about drugs known to cause AKI. The DAKI-KG is integrated from several credible domain sources, which we standardized to international vocabularies. We demonstrate the applicability of the DAKI-KG through competency questions and expert evaluations. The envisioned two application areas are: (1) the KG can be used as input for a decision support tool to get insights into potential side-effects and drug-drug interactions harmful for kidneys, especially in patients with chronic kidney disease and (2) researchers can utilize the KG to derive confounders and variables for (causal) machine learning models or as input into link prediction methods. The evaluation results show the applicability of our KG through varying competency questions and use cases, though further improvements are needed before it is ready for clinical application. We anticipate the DAKI-KG to be relevant for tasks such as confounder identification, variable analysis, and the development of QA systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.