TIME: A Taylor-Inspired Mixed-Effects Model for IDH Prediction

Background: Intradialytic hypotension (IDH) is a critical complication in hemodialysis that increases morbidity and treatment risks, yet existing machine learning methods inadequately address session-level physiological state heterogeneity and fail to balance linear and nonlinear feature interactions, limiting predictive accuracy and interpretability. Methods: We developed TIME (Taylor-Inspired Mixed-Effects Model), which explicitly separates linear interactions, higher-order nonlinear dependencies, and residual terms inspired by Taylor series expansion, while incorporating a physiological state embedding layer to capture session-level physiological state heterogeneity revealed through hierarchical clustering on autoencoder-derived latent representations of hematologic indicators selected using SHAP analysis and clinical knowledge. Using data from 532 hemodialysis patients (18,309 records), we compared TIME against 19 state-of-the-art baseline models, including blood pressure-stratified subcohort and out-of-distribution analyses. Results: Hierarchical clustering identified two distinct hematological states with different IDH risks (57.93% vs. 55.75%, p=6.60 × 10^-7). TIME achieved 0.6929 accuracy, 0.7435 F1-score, and 0.3663 MCC on the full cohort, the highest AUC across all three subcohorts (0.7010, 0.6795, and 0.7201), and strong out-of-distribution performance (accuracy 0.7053, F1 0.7377, MCC 0.4009). Integrating TIME's architecture into baseline models improved MCC by 2.67 percentage points on average. Conclusions: TIME effectively predicts IDH by modeling session-level physiological state heterogeneity and balancing feature interactions, with robust generalizability across diverse populations. Significance: TIME advances precision medicine in dialysis care by enabling early risk identification and personalized treatment strategies, while revealing broader immune, nutritional, and fluid-related markers of IDH risk.