Where Is the Patient Heading? Multi-Path Forecast-Aware Mortality Prediction in the ICU

Mortality prediction in the intensive care unit (ICU) requires estimating risk from the patient history available at a given decision time. Although existing predictors can model longitudinal observations and learn associations with future outcomes, they need not explicitly represent anticipated physiological changes or incorporate the outcomes of similar patients. We investigate whether explicit physiological forecasts and retrieved patient outcomes improve prediction under matched observation windows. We propose TRACE (**T**rajectory-**R**etrieval **A**ugmented **C**linical **E**stimation), which forecasts quantiles of near-future vital signs and uses the predicted median and interval width as features for mortality classification. A complementary retrieval pathway estimates risk from the observed outcomes of similar training patients, and the two estimates are combined through entropy-conditioned weighting. Across seven forecasting backbones on MIMIC-IV and eICU-CRD, TRACE improves discrimination relative to matched observed-history and point-forecast controls. Component comparisons associate both quantile-derived features and outcome retrieval with further gains, and the observed benefit over matched controls extends across the evaluated decision times. On a matched MIMIC-IV task, TRACE also achieves the highest AUROC and AUPRC among the mortality predictors evaluated. These findings support explicit physiological forecasting and outcome retrieval as complementary components for ICU mortality prediction in the studied settings.