ArticleNature communications2025
NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Enriching patient populations in ICU trials: reducing heterogeneity through machine learning.Current opinion in critical care · 2025Review
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20 authors.
Funding
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 comparison, XGBoost achieved AUROC of 0.58 (95% CI: 0.58 - 0.59) and AUPRC of 0.48 (95% CI: 0.46 - 0.50). This approach may help clinicians personalize nutritional therapy in critical care.
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