Predicting Disease Progression in Critically Ill Patients Using Frequency-Enhanced Time-series Forecasting

Accurate disease progression prediction is vital for managing critically ill patients in intensive care. Existing deep learning approaches mainly operate in the time domain and often fail to capture long-range dependencies and spectral dynamics. This study proposes a unified framework integrating time and frequency-domain representations to improve predictive accuracy. We introduce FETT (Frequency-Enhanced TCN-Transformer), a dual-domain forecasting framework that combines discrete wavelet transform–based frequency analysis with transformer-based temporal modeling. Based on an iTransformer backbone, FETT introduces three key innovations: (1) a frequency-aware representation module, (2) a dual-TCN architecture that enhances temporal representation through multi-scale feature extraction and global dependency modeling; and (3) a frequency-aware inverse reconstruction module for clinically interpretable time-domain forecasts. Experiments on the MIMIC-IV Sepsis-3 cohort show that FETT outperforms state-of-the-art baselines, reducing MSE by up to 20.81% and MAE by 13.55% in 24-hour forecasting tasks. Ablation studies confirmed the complementary contributions of the dual-TCN design and frequency-aware modules. FETT effectively integrates time- and frequency-domain information to deliver accurate and interpretable disease progression predictions in critical care. By bridging spectral and temporal representations, it enables early detection of patient deterioration and holds strong potential for advancing proactive ICU monitoring and personalized clinical decision-making.