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Updated: Oct 5, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Machine Learning, Clinical Notes and Knowledge Graphs for Early Prediction of Acute Kidney Injury in the Intensive
Iacopo Vagliano1, Wei-Hsiang Hsu1, Martijn C Schut1
1Dept. of Medical Informatics, Amsterdam UMC, Location AMC, The Netherlands.
Abstract:
Acute kidney injury (AKI) is an abrupt decrease of kidney function which is common in the intensive care. Many AKI prediction models have been proposed, but an analysis of what is the added value of clinical notes and medical terminologies has not yet been conducted. We developed and internally validated a model to predict AKI that includes not only clinical variables, but also clinical notes and medical terminologies. Our results were overall good (AUROC > 0.80). The best model used only clinical variables (AUROC 0.899).
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