ArticleFrontiers in nutrition2023
Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.
Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities.Nutrients · 2026Review
- Review
- Artificial Intelligence in Parenteral Nutrition: Enhancing Patient Outcomes Through Global Experience and the Bulgarian Context.Nutrients · 2026Review
- Review
- An interpretable AutoML-based prediction model for enteral nutrition intolerance in severe pulmonary tuberculosis patients and development of a clinical decision system.Frontiers in nutrition · 2026Article
- AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap.Nutrients · 2025Review
- Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.BMC medical informatics and decision making · 2025Observational
- Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking.BMC pharmacology & toxicology · 2025Article
- Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma.Journal of translational medicine · 2024Article
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Early versus delayed enteral nutrition in ICU patients with sepsis: a propensity score-matched analysis based on the MIMIC-IV database.Frontiers in nutrition · 2024Article
- LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients.Therapeutics and clinical risk management · 2024Article
- Enhancing sepsis therapy: the evolving role of enteral nutrition.Frontiers in nutrition · 2024Review
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Authors and funding
7 authors.
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Abstract
Background: This study applied machine learning (ML) algorithms to construct a model for predicting EN initiation for patients in the intensive care unit (ICU) and identifying populations in need of EN at an early stage. Methods: This study collected patient information from the Medical Information Mart for Intensive Care IV database. All patients enrolled were split randomly into a training set and a validation set. Six ML models were established to evaluate the initiation of EN, and the best model was determined according to the area under curve (AUC) and accuracy. The best model was interpreted using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm and SHapley Additive exPlanation (SHAP) values. Results: A total of 53,150 patients participated in the study. They were divided into a training set (42,520, 80%) and a validation set (10,630, 20%). In the validation set, XGBoost had the optimal prediction performance with an AUC of 0.895. The SHAP values revealed that sepsis, sequential organ failure assessment score, and acute kidney injury were the three most important factors affecting EN initiation. The individualized forecasts were displayed using the LIME algorithm. Conclusion: The XGBoost model was established and validated for early prediction of EN initiation in ICU patients.
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