Eigen-guided transformer: A data-driven approach for chronic kidney disease forecasting

Accurate prediction of Chronic Kidney Disease (CKD) development is essential for prompt therapeutic intervention; nevertheless, it is difficult due to the highly individualized disease trajectories and intricate multivariate risk profiles. Contemporary deep learning models provide substantial predictive capability; yet, they frequently exhibit computational inefficiency and a lack of interpretability, akin to a "black box." To overcome these limitations, we present the Eigen-Guided Transformer, an innovative architecture that systematically enhances model topology by data-driven eigenvalue analysis of clinical feature correlations. Our methodology independently establishes: (1) the optimal quantity of attention heads by ascertaining the intrinsic dimensionality necessary to account for 95% of data variance (h = 11), (2) an eigenvector-informed weight initialization to encapsulate a priori feature interactions, and (3) validation-driven depth optimization. Upon assessing daily-aggregated longitudinal sequences from the MIMIC-IV dataset for creatinine prediction, the model attained a Mean Squared Error (MSE) of 0.089 ± 0.004 and a Mean Absolute Error (MAE) of 0.132 + 0.0001, indicating an 11.1% enhancement over the most robust baseline (TFT). The Eigen-Guided Transformer exhibited exceptional parameter efficiency, achieving a FLOPs/accuracy ratio of 142.4M, markedly lower than that of TFT (300M) and Autoformer (1.0B). External validation of the eICU dataset demonstrated strong cross-institutional generalizability (MSE 0.0117 ± 0.0001; MAE 0.0254 + 0.0008). To guarantee clinical dependability, we incorporated Monte Carlo Dropout for uncertainty quantification, resulting in precisely calibrated prediction intervals with 95.8% observed coverage. Additionally, a demographic fairness audit encompassing age, gender, and ethnicity demonstrated equitable performance, with a maximum discrepancy ratio of merely 1.32. The Eigen-Guided Transformer offers a transparent, efficient, and reliable paradigm for the individualized therapy of chronic renal disease by matching attention mechanisms with the intrinsic biological structure of clinical data.