OmniTFT: an EHR-driven multi-target forecasting model for ICU clinical decision support systems

BackgroundAccurate multivariate time-series forecasting of vital signs and laboratory results is crucial for electronic health record (EHR)–driven clinical decision support systems in intensive care units (ICUs). However, vital signs are susceptible to noise and exhibit rapid fluctuations, while laboratory results are characterized by missing values and measurement lags, making it challenging to forecast heterogeneous ICU variables within a common framework.MethodsWe introduce OmniTFT, a Temporal Fusion Transformer-based multi-target forecasting model for ICU EHR time series. OmniTFT combines state-balanced sliding-window sampling with three training regularizers: frequency-aware embedding shrinkage, historical group-entropy regularization, and shock-alignment regularization. A shared backbone with target-specific heads enables multiple forecasting tasks within a common training and inference framework while preserving target-specific temporal resolutions and forecast horizons. OmniTFT was trained on MIMIC-IV (n = 13,277) and independently validated on two cohorts: the MIMIC-III CareVue cohort (n = 4,188) and the eICU cohort (n = 23,238). Component ablation and sampling sensitivity analyses were conducted using a fixed patient partition and ten different training seeds.ResultsOn the MIMIC-IV test set, OmniTFT achieved the lowest MAE for five of eight targets, ranked second for creatinine and respiratory rate, and ranked third for temperature. In a retrospective analysis in eICU, adding the OmniTFT forecast of the subsequent unobserved 24 h to the complete observed record improved discrimination for incident moderate-to-severe acute kidney injury compared with the observed record alone (AUROC 0.763, 95% CI: 0.674–0.852 vs. 0.693, 0.598–0.787, ΔAUROC + 0.070, DeLong p = 0.009), and identified 73.3% vs. 57.8% of subsequent events when 30% of patients were flagged for review.ConclusionsOmniTFT provides a shared-backbone framework for forecasting heterogeneous ICU variables while preserving target-specific temporal configurations. Retrospective results in disease-enriched cohorts support broader external validation and suggest its potential as an EHR-integrated forecasting component for clinical decision support.