Missingness-Aware Prior-Data Fitted Network for Irregular Clinical Time-Series
Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora, Pietro Bosoni, Simon Lebech Cichosz, Arianna Dagliati, Arijit Khan, Riccardo Bellazzi
Abstract
Clinical time series are irregularly observed with laboratory measurements recorded at uneven intervals and observation patterns often reflecting patient state, clinical concern, and site-specific practice. Consequently, missingness is not merely a nuisance factor but an informative signal for prediction. We propose a Missingness-Aware Prior-Data Fitted Network (MA-PFN) for irregular clinical time-series prediction. MA-PFN first learns a diffusion-based prior over joint laboratory value and observation-mask trajectories from source-domain electronic health records, capturing both physiological dynamics and measurement processes. Synthetic trajectories sampled from this prior are then transformed into context-query tasks to train a PFN-style conditional predictor. Once trained, the model performs prediction for new admissions in a single forward pass, requiring neither retraining nor parameter adaptation on target datasets. The framework naturally supports both imputation and forecasting through different context-query constructions. We conduct internal validation on a held-out MIMIC-III test set and external validation on eICU, evaluating both imputation and forecasting and assessing cross-domain generalization under shifts in patient populations, clinical practices, and observation mechanisms. MA-PFN achieves the best performance across all evaluated settings, with Overall/Macro nRMSD ranging from (0.193/0.186) for internal imputation to (0.297/0.297) for external forecasting, without target-domain adaptation. These results demonstrate that learning missingness-aware task priors enables accurate and transferable clinical time-series prediction under informative missingness.