ArticleAsia Pacific journal of clinical nutrition2025
Artificial intelligence assisted nutritional risk evaluation model for critically ill patients: Integration of explainable machine learning in intensive care nutrition.
Article in Asia Pacific journal of clinical nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Integrating gut‑brain axis insights into bundled nutrition care for critically ill patients: From mechanisms to implementation (Review).Molecular medicine reports · 2026Review
- Construction of risk prediction models for psoriasis based on 45 dietary nutrients using machine learning and SHAP analysis.Asia Pacific journal of clinical nutrition · 2026Article
- Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Body Mass Index and Survival in Children Receiving Extracorporeal Membrane Oxygenation.JAMA network open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
objectivesCritically ill patients require individualized nutrition support, with assessment tools like Nutrition Risk Screening 2002 and Nutrition Risk in the Critically Ill scores. Challenges in continu-ous nutrition care prompt the need for innovative solutions. This study develops an artificial intelligence assisted nutrition risk evaluation model using explainable machine learning to support intensive care unit dietitians. METHODS AND STUDY
designEthical approval was obtained for a retrospective analysis of 2,122 pa-tients. Nutrition risk assessment involved six dietitians, with 1,994 patients assessed comprehensively. Artificial intelligence models and shapley additive explanations analysis were used to predict and understand nutrition risk.
resultsHigh nutrition risk (35.2%) correlated with elder age, lower body weight, BMI, albumin, and higher disease severity. The AUROC scores achieved by XGBoost (0.921), CatBoost (0.926), and LightGBM (0.923) were superior to those of Logistic Regression. Key features influencing nutrition risk included Acute Physiology and Chronic Health Evaluation II score, albumin, age, BMI, and haemoglobin.
conclusionsThe study introduces an artificial intelligence assisted nutrition risk evaluation model, offering a promising avenue for continuous and timely nutrition support in critically ill patients. External validation and exploration of feature relationships are needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.