Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury

TL;DR

This paper introduces CT-Former, a novel causal-transformer model that effectively predicts acute kidney injury early by handling irregular data and providing interpretability through causal pathways.

Contribution

The paper presents a continuous-time, causal-transformer framework with a causal-attention module for transparent and accurate early AKI prediction.

Findings

CT-Former outperforms state-of-the-art models on MIMIC-IV data.

It provides interpretable causal pathways linking physiological shocks to AKI risk.

The model handles irregular data without artificial imputation.

Abstract

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.