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Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury

Key takeaways

Researchers pooled machine learning models trained on the medical records of 785,497 adults across several health systems to find what drives sudden kidney injury. Rather than just listing risk factors, they looked for the specific lab values and body measurements where risk starts to climb, and how those factors combine. The data come from past records, so the findings show links and thresholds, not proof of cause. This is a preprint, meaning other scientists have not reviewed it yet.

Source

Preprint · medRxiv nephrology preprints · 2026-09-02
https://www.medrxiv.org/content/10.64898/2026.08.31.26361849v1?rss=1