ReviewFrontiers in nutrition2020
Malnutrition, Health and the Role of Machine Learning in Clinical Setting.
Review in Frontiers in nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
What it found
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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
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.International journal of environmental research and public health · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- At the Intersection of Gut Microbiome and Stroke: A Systematic Review of the Literature.Frontiers in neurology · 2021Pooled it
- Predictive ability of malnutrition screening tools in enterally fed, mechanically ventilated patients with phase angle inference: A prospective observational study.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026Article
- Role of artificial intelligence in determining nutritional risk factors among post-periodontal surgical patients. A scoping review.Frontiers in oral health · 2026Review
- Artificial intelligence assisted nutritional risk evaluation model for critically ill patients: Integration of explainable machine learning in intensive care nutrition.Asia Pacific journal of clinical nutrition · 2025Article
- Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.Nutrients · 2025Review
- A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery.Health information science and systems · 2024Article
- Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren.Frontiers in epidemiology · 2023Article
- Health management of malnourished elderly in primary health care: a scoping review.BMC primary care · 2022Article
- Malnutrition in Hospitalized Old Patients: Screening and Diagnosis, Clinical Outcomes, and Management.Nutrients · 2022Review
- On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.Frontiers in public health · 2022Review
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
- Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.The Journal of nutrition · 2021Article
- Racial, Economic, and Health Inequality and COVID-19 Infection in the United States.Journal of racial and ethnic health disparities · 2021Article
- Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.Frontiers in neurology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immune-related disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that ~10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60-80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition.
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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.