Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

查看全文
  • Mateen Jangda,
  • Jayshil Patel,
  • Akhil Vaid,
  • Jaskirat Gill,
  • Paul McCarthy,
  • Jacob Desman,
  • Rohit Gupta,
  • Dhruv Patel,
  • Nidhi Kavi,
  • Shruti Bakare,
  • Eyal Klang,
  • Robert Freeman,
  • Anthony Manasia,
  • John Oropello,
  • Lili Chan,
  • Mayte Suarez-Farinas,
  • Alexander W. Charney,
  • Roopa Kohli-Seth,
  • Girish N. Nadkarni,
  • Ankit Sakhuja

摘要

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.