Abstract
Acute Kidney Injury (AKI) is a common clinical syndrome with poor prognosis and high mortality in the intensive care unit (ICU). Delayed diagnosis limits timely intervention and worsens outcomes, while early recognition is further challenged by the imbalanced distribution of AKI and non-AKI cases. A Dual-Channel Bilinear Mapping Triplet (DCBM-Tri) model was proposed for early AKI recognition, which used contrastive learning to enhance patient representations by capturing latent clinical features and improving discriminability in high-dimensional space. To identify clinically meaningful risk factors, SHAP-based interpretability analysis was further applied. The 12-hour-ahead prediction setting (AKI: non-AKI = 537: 2339) provided an optimal balance between discriminative performance and positive case identification. DCBM-Tri showed statistically significant improvements over conventional baselines, including LSTM- and resampling-based methods. However, no statistically significant improvement in AUPRC was observed over the feature-channel ablation model. Moreover, decision curve analysis demonstrated that DCBM-Tri provided a broader range of net clinical benefit across relevant risk thresholds. SHAP analysis further identified the top five contributing features as C-reactive protein, ionized calcium, bicarbonate, pH, and sodium. Overall, DCBM-Tri effectively addresses class imbalance in early AKI prediction by learning discriminative patient similarities, leading to improved sensitivity for high-risk patients. Its interpretable outputs further provide clinically meaningful signals to support early recognition and potential individualized prevention.