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Malnutrition

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Role of irisin and myostatin on sarcopenia in malnourished patients diagnosed with GLIM criteria.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVES: Sarcopenia is characterized by the loss of muscle mass. Skeletal muscle can produce and secrete different molecules called myokines. Irisin and myostatin are antagonistic myokines, and to our knowledge, no studies of both myokines have be...

Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Clinical nutrition (Edinburgh, Scotland)
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 (...

Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients.

Nutrients
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...

Predicting malnutrition from longitudinal patient trajectories with deep learning.

PloS one
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...

Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.

Scientific reports
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...

Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data.

Clinical nutrition (Edinburgh, Scotland)
OBJECTIVE: The aim of this study is using clinical factors and non-enhanced computed tomography (CT) deep features of the psoas muscles at third lumbar vertebral (L3) level to construct a model to predict malnutrition in gastric cancer before surgery...

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in re...

Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.

Studies in health technology and informatics
BACKGROUND: Malnutrition in hospitalised patients can lead to serious complications, worse patient outcomes and longer hospital stays. State-of-the-art screening methods rely on scores, which need additional manual assessments causing higher workload...

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weig...

Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence.

Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition
The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could ...