Context-Adaptive Flow Matching for ICU Time-Series Generation

Synthetic ICU time-series data can support clinical modeling, but data that help train a forecaster may still distort temporal dependence, spectral structure, or physiological relationships. We propose OU-WSP FM, a flow-matching framework for conditional ICU data generation with a structured endpoint prior. Its Ornstein–Uhlenbeck-inspired autoregressive component models local mean-reverting dynamics, while its Woodbury-spectral component extrapolates the remaining context residual using a low-rank Fourier basis. Together, these components incorporate context-dependent temporal structure into the source distribution without dense covariance inversion. Experiments on MIMIC-III and MIMIC-IV evaluate forecasting utility, statistical fidelity, and blood-pressure ordering, with ablations of spectral strength, channel sharing, and network architecture. The GRU and TCN experiments report composite forecasting errors \(22.0%\)–\(27.2%\) lower than Gaussian FM. To distinguish the effects of endpoint design and calibration, we conduct detailed ablation experiments and calibration analyses under context-only imputation. These findings clarify the distinct roles of structured endpoints, calibration, and clinical safeguards in ICU time-series generation.