AI could help predict nutrition risks in ICU patients, study finds

A new study by researchers at the Icahn School of Medicine at Mount Sinai suggests that artificial intelligence (AI) could help predict which critically ill patients on ventilators are at risk of underfeeding, potentially enabling clinicians to adjust nutrition early and improve patient care. Details of the study were published in the December 17 online issue of Nature Communications.

The first week on a ventilator is especially important for providing proper nutrition, since patients’ needs often shift quickly during this period, say the investigators. “Too many patients on ventilators in the intensive care unit (ICU) don’t get the nutrition they need during the critical first week,” says co-senior corresponding author Ankit Sakhuja, MBBS, MS, Associate Professor of Artificial Intelligence and Human Health, and Medicine (Data-Driven and Digital Medicine). “Their needs are changing rapidly, and it’s easy for them to fall behind. We wanted to explore a simple, timely way to identify who is most at risk of being underfed so that clinicians could intervene earlier, adjust care, and make sure each patient receives the right support when it matters most.”

The research team built an AI tool, called NutriSightT, which analyzed routine ICU data such as vital signs, lab results, medications, and feeding information to predict, hours in advance, which patients may be underfed on days 3–7 of ventilation. Using large deidentified ICU datasets from Europe and the United States, the model was trained and validated to update predictions every four hours as patient conditions change.

The study identified several key insights that could potentially help guide patient care:

• Underfeeding is common early in ICU care. About 41 percent to 53 percent of patients were underfed by day three, and 25-35 percent remained underfed by day seven.
• The model is dynamic and interpretable, showing which routine factors—such as blood pressure, sodium levels, or sedation—influence underfeeding risk.

• The research could support personalized feeding plans, guide nutrition teams, and inform clinical trials to determine the most effective nutrition strategies for individual patients.

The investigators emphasize that NutriSighT would not be intended to replace clinicians. Instead, it could serve as an early-warning system to help guide timely nutrition interventions.

The research team’s next steps include prospective multi-site trials to test whether acting on these predictions improves patient outcomes, careful integration into electronic health records, and expansion to broader individualized nutrition targets.

“The significance of our study’s findings is that, for the first time, it may be possible to identify which patients are at risk of underfeeding early in their ICU stay and tailor care to their individual needs,” says co-senior author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Director of the Hasso Plattner Institute for Digital Health, and Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai, and Chief AI Officer of the Mount Sinai Health System. “It represents an important step towards giving clinicians better information to make decisions about nutrition. Ultimately, the goal is to provide the right amount of nutrition to the right patient at the right time, which could help improve recovery and outcomes in critically ill patients and lay the groundwork for more personalized care strategies.”

Reference:

Jangda, M., Patel, J., Vaid, A. et al. NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients. Nat Commun 16, 11189 (2025). https://doi.org/10.1038/s41467-025-66200-1