Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge

Acute Kidney Injury (AKI) is a common complication in hospitalized patients and is associated with increased in-hospital mortality, readmission, and chronic kidney disease. Early identification of patients at risk of AKI non-recovery can improve discharge planning and follow-up. Using a retrospective cohort of 7,667 patient encounters diagnosed with AKI from the University of California San Diego Health, we compared traditional machine learning (ML) and temporal deep learning (DL) models to predict three AKI recovery outcomes: Recovery, Partial Recovery, and Non-Recovery. The ML models evaluated were Logistic Regression, Random Forest, and XGBoost; while, the DL models were Gated Recurrent Unit (GRU) and Long-Short Term Memory. On the test set, DL models consistently outperformed traditional ML approaches. The GRU model achieved the highest Macro-Area Under the Curve (AUC) (0.822) with strong discrimination for the Non-Recovery class (AUC 0.932). This work demonstrates that temporal modeling of clinical trajectories can enhance AKI recovery prediction.