RenalTransLSTM: Multi-Horizon Prediction of Acute Kidney Injury in ICU Patients using a Hybrid LSTM-Transformer Architecture
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Objective
Acute kidney injury (AKI) affects a large proportion of patients in the intensive care unit (ICU) and is a major contributor to morbidity, mortality, and cost. Although electronic health records (EHRs) capture rich longitudinal data, many predictive models fail to detect AKI early enough for effective intervention. Non-temporal methods such as logistic regression and XGBoost treat patient history as aggregated risk factors, discarding the temporal evolution of clinical state. A recent trend is to employ temporal models, such as recurrent neural networks, to capture sequential patterns, but these models struggle with irregular sampling and limited long-range contextual awareness. To address the challenge, we propose RenalTransLSTM, a hybrid temporal deep learning framework for early, multi-horizon AKI prediction and identification of modifiable risk factors.
Methods
RenalTransLSTM integrates Long Short-Term Memory (LSTM) networks with Transformer encoders to model both local temporal dynamics and global contextual depen-dencies in ICU time-series data. Using 48-hour patient histories from MIMIC-IV (61,735 admissions), the model predicts AKI at 6-, 12-, and 24-hour lead times. We benchmark the model against SVM, XGBoost, LSTM, TG-LSTM, and a Transformer, and apply Integrated Gradients and counterfactual analysis to identify modifiable risk factors.
Results
RenalTransLSTM outperforms all baselines across most horizons and metrics, achiev-ing AUROC above 0.90 and F1-scores reaching 0.85 while maintaining balanced precision and recall on imbalanced data. Ablation studies confirm that combining LSTM and Transformer components improved robustness and predictive performance. Counterfactual analysis identifies clinically meaningful, modifiable risk factors associated with AKI progression.
Conclusion
RenalTransLSTM offers an effective, interpretable framework for early AKI prediction in the ICU, supporting proactive intervention and clinical decision support.
