NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients.

ABSTRACT: Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We develop NutriSighT, a transformer model using learnable positional encodings to predict which patients would be underfed (receive less than 70% daily caloric requirements) between days 3-7 of mechanical ventilation and compared its performance against XGBoost. Using retrospective data from two ICU databases (3284 patients from AmsterdamUMCdb for development and 6456 from MIMIC-IV for external validation), we included adults mechanically ventilated for at least 72 h. NutriSighT achieved AUROC of 0.81 (95% CI: 0.81 - 0.82) and AUPRC of 0.70 (95% CI: 0.70 - 0.72) internally. External validation yielded AUROC of 0.76 (95% CI: 0.75 - 0.76) and an AUPRC of 0.70 (95% CI: 0.69 - 0.70). In c

SUBMITTER: Jangda M 

PROVIDER: S-EPMC12711878 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature