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Dynamic machine learning architecture integrating time-phased predictions for acute kidney injury following extracorporeal membrane oxygenation

Article C Peer reviewed Observational

Key takeaways

This study used computer models to predict kidney injury in 344 patients on life support. The models could flag patients at risk before the injury happened. This is a tool for prediction, not a test of a new treatment, so it shows a link but not proof of cause.

Who did this work

Jianhai Lu (The First People's Hospital of Shunde) · Zhen Chen (The First People's Hospital of Shunde) · Donghua Zheng (The First Affiliated Hospital, Sun Yat-sen University) · Genglong Liu (Southern Medical University) · Yijia Wu (The First Affiliated Hospital, Sun Yat-sen University) · Wanfang Li (The First Affiliated Hospital, Sun Yat-sen University) · Liuer Zuo (The First People's Hospital of Shunde) · Qiang Tai (The First Affiliated Hospital, Sun Yat-sen University)

Source

Article · OpenAlex, Kidney disease concept sweep (verified C2778653478) · 2026-10-22
https://doi.org/10.3389/fphys.2026.1922656