Advancing Continuous Prediction for Acute Kidney Injury via Multi-task Learning: Towards Better Clinical Applicability

Acute kidney injury (AKI) presents a public health challenge with profound short and long-term morbidity and mortality. Early prediction and severity identification of AKI are crucial for improving clinical outcomes through timely interventions and efficient resource allocation. Previous studies have predominantly focused on serum creatinine, neglecting the significance of urine output, which, combined with the delayed rise in serum creatinine post-AKI onset, hinders the timely detection of AKI. To address these shortcomings, we propose a novel multi-task learning approach incorporating a continuous urine output monitoring strategy, predicting AKI onset and stage within 6-hour intervals up to 48 hours. Our model exhibits strong performance with the area under the receiver operating characteristic curve of 99.3% and an area under the precision-recall curve of 99.0% for predicting AKI within 48 hours. Also, our model is able to capture overall disease trends perfectly for 35.7% of the AKI cohort and 94.8% of the disease-free cohort. The proposed approach enhances clinical applicability, providing insights into disease dynamics.