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CT-Former, a novel architecture integrating continuous-time modeling with a Causal-Transformer, is introduced for early prediction of Acute Kidney Injury (AKI). It addresses data irregularity by tracking patient temporal trajectories and resolves the black-box problem by generating a directed structural causal matrix to identify the historical onset of physiological shocks. Experiments on the MIMIC-IV cohort demonstrate that CT-Former outperforms state-of-the-art baselines, providing an accurate and interpretable tool for clinical decision-making.
Finally, a deep learning model for AKI prediction that doesn't just predict, but tells you *why*, by tracing the causal chain of physiological events.
Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle with irregularly sampled data and suffer from the opaque"black-box"nature of sequential architectures, strictly limiting clinical trust. To address these challenges, we propose CT-Former, integrating continuous-time modeling with a Causal-Transformer. To handle data irregularity without biased artificial imputation, our framework utilizes a continuous-time state evolution mechanism to naturally track patient temporal trajectories. To resolve the black-box problem, our Causal-Attention module abandons uninterpretable hidden state aggregation. Instead, it generates a directed structural causal matrix to identify and trace the exact historical onset of severe physiological shocks. By establishing clear causal pathways between historical anomalies and current risk predictions, CT-Former provides native clinical interpretability. Training follows a decoupled two-stage protocol to optimize the causal-fusion process independently. Extensive experiments on the MIMIC-IV cohort (N=18,419) demonstrate that CT-Former significantly outperforms state-of-the-art baselines. The results confirm that our explicitly transparent architecture offers an accurate and trustworthy tool for clinical decision-making.
