Observational studyJournal of medical Internet research2023
Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study.
Observational study in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Diagnostic and application guidelines for malnutrition in adult patients (2025 edition).Asia Pacific journal of clinical nutrition · 2025Guideline
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- A Maturation-Aware Machine Learning Framework for Screening the Nutritional Status of Adolescents.Nutrients · 2026Article
- Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.Asia Pacific journal of clinical nutrition · 2026Article
- Exploring Risk Factors Related to Low Calf Circumference in Older Adults With Multimorbidity: Cross-Sectional Latent Class Analysis.JMIR aging · 2025Article
- 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
- Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.Clinical kidney journal · 2025Article
- An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal and external validation study.Frontiers in nutrition · 2025Article
- Investigating Frailty, Polypharmacy, Malnutrition, Chronic Conditions, and Quality of Life in Older Adults: Large Population-Based Study.JMIR public health and surveillance · 2024Article
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- Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.Frontiers in nutrition · 2024Article
- Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning-Based Analysis.JMIR public health and surveillance · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOlder patients are at an increased risk of malnutrition due to many factors related to poor clinical outcomes.
objectiveThis study aims to develop an assisted diagnosis model using machine learning (ML) for identifying older patients with malnutrition and providing the focus of individualized treatment.
methodsWe reanalyzed a multicenter, observational cohort study including 2660 older patients. Baseline malnutrition was defined using the global leadership initiative on malnutrition (GLIM) criteria, and the study population was randomly divided into a derivation group (2128/2660, 80%) and a validation group (532/2660, 20%). We applied 5 ML algorithms and further explored the relationship between features and the risk of malnutrition by using the Shapley additive explanations visualization method.
resultsThe proposed ML models were capable to identify older patients with malnutrition. In the external validation cohort, the top 3 models by the area under the receiver operating characteristic curve were light gradient boosting machine (92.1%), extreme gradient boosting (91.9%), and the random forest model (91.5%). Additionally, the analysis of the importance of features revealed that BMI, weight loss, and calf circumference were the strongest predictors to affect GLIM. A BMI of below 21 kg/m2 was associated with a higher risk of GLIM in older people.
conclusionsWe developed ML models for assisting diagnosis of malnutrition based on the GLIM criteria. The cutoff values of laboratory tests generated by Shapley additive explanations could provide references for the identification of malnutrition.
trial registrationChinese Clinical Trial Registry ChiCTR-EPC-14005253; https://www.chictr.org.cn/showproj.aspx?proj=9542.
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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.