AIMC Topic: Malnutrition

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Predicting malnutrition from longitudinal patient trajectories with deep learning.

PloS one Jul 28, 2022
Malnutrition is common, morbid, and often correctable, but subject to missed and delayed diagnosis. Better screening and prediction could improve clinical, functional, and economic outcomes. This study aimed to assess the predictability of malnutriti...
Humans Cross-Sectional Studies Deep Learning Logistic Models Emergency Service, Hospital Malnutrition
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Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.

Scientific reports Jan 7, 2022
Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food est...
Image Processing, Computer-Assisted Predictive Value of Tests Malnutrition Eating Humans Reproducibility of Results Diet Deep Learning Meals Nursing Homes Photography Long-Term Care Nutritive Value Early Diagnosis Automation Nutritional Status
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Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients.

Nutrients Dec 17, 2021
Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for diet...
Artificial Intelligence Hospitalization Aged Nutrition Assessment Humans Meals Inpatients Malnutrition Eating Feeding Behavior Diet Energy Intake Image Processing, Computer-Assisted
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Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Clinical nutrition (Edinburgh, Scotland) Nov 10, 2021
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...
Malnutrition Postoperative Period Heart Defects, Congenital Clinical Decision Rules Humans Infant ROC Curve Machine Learning Predictive Value of Tests Algorithms Prospective Studies Female Male Postoperative Complications
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A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition.

Nutrients Nov 10, 2021
BACKGROUND: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low r...
Diagnostic Imaging Malnutrition Japan Deglutition Disorders Male Body Mass Index Sarcopenia Mental Recall Pneumonia, Aspiration Recognition, Psychology Humans Machine Learning Algorithms Female Aged Aged, 80 and over Cross-Sectional Studies
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Multivariate random forest prediction of poverty and malnutrition prevalence.

PloS one Sep 8, 2021
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models ofte...
Humans Machine Learning Prevalence Developing Countries Poverty Malnutrition Multivariate Analysis Social Problems
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Towards personalized nutritional treatment for malnutrition using machine learning-based screening tools.

Clinical nutrition (Edinburgh, Scotland) Aug 25, 2021
Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional therapy. Yet, despite the presence of many nutrition screening tools for use in the hospital setting, ther...
Nutritional Support Machine Learning Mass Screening Humans Malnutrition Artificial Intelligence Nutritional Status
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A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

Clinical nutrition (Edinburgh, Scotland) Jul 3, 2021
BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This s...
Diagnosis, Computer-Assisted China Logistic Models Cluster Analysis Functional Status Workflow Feasibility Studies Malnutrition Nutrition Assessment Neoplasms Quality of Life Severity of Illness Index Humans Adult Nutritional Status Machine Learning Male Female Middle Aged Aged
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Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh.

Nutrition (Burbank, Los Angeles County, Calif.) May 15, 2020
OBJECTIVE: The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms.
Support Vector Machine Malnutrition Bangladesh Humans Machine Learning Algorithms Child
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Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study.

Computers in biology and medicine Jun 1, 2025
Malnutrition, affecting both adults and children globally, results from inadequate nutrient intake or loss of body mass. Traditional screening tools, reliant on detailed questionnaires, are costly, time-consuming, and often lack accuracy and generali...
Female Humans Benchmarking Infant, Newborn Neural Networks, Computer Male Machine Learning Nutrition Assessment Malnutrition Intensive Care Units, Neonatal
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