Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury
This provides a more accurate and interpretable tool for clinical decision-making in healthcare, though it is an incremental improvement over existing transformer-based methods.
The paper tackled the problem of early prediction of Acute Kidney Injury (AKI) from irregularly sampled clinical data, proposing CT-Former which integrates continuous-time modeling with a Causal-Transformer to achieve state-of-the-art performance on the MIMIC-IV cohort (N=18,419).
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.