[HTML][HTML] Development and assessment of assisted diagnosis models using machine learning for identifying elderly patients with malnutrition: cohort study

X Wang, F Yang, M Zhu, H Cui, J Wei, J Li… - Journal of Medical …, 2023 - jmir.org
Background Older patients are at an increased risk of malnutrition due to many factors
related to poor clinical outcomes. Objective This study aims to develop an assisted …

[HTML][HTML] Machine learning-based prediction of in-hospital complications in elderly patients using GLIM-, SGA-, and ESPEN 2015-diagnosed malnutrition as a factor

SS Ren, MW Zhu, KW Zhang, BW Chen, C Yang… - Nutrients, 2022 - mdpi.com
Background: Malnutrition is prevalent in elderly inpatients and is associated with various
adverse outcomes during their hospital stay, but the diagnosis of malnutrition still lacks …

[HTML][HTML] Explainable AI for malnutrition risk prediction from m-health and clinical data

F Di Martino, F Delmastro, C Dolciotti - Smart Health, 2023 - Elsevier
Malnutrition is a serious and prevalent health problem in the older population, and
especially in hospitalised or institutionalised subjects. Accurate and early risk detection is …

Malnutrition risk assessment in frail older adults using m-health and machine learning

F Di Martino, F Delmastro… - ICC 2021-IEEE …, 2021 - ieeexplore.ieee.org
Malnutrition represents a major public health con-cern worldwide, and it particularly harms
older adults since it is frequently associated with several chronic health disorders. It …

[HTML][HTML] Machine Learning-Based Prediction of Complications and Prolonged Hospitalization with the GLIM Criteria Combinations Containing Calf Circumference in …

SS Ren, KW Zhang, BW Chen, C Yang, R Xiao, PG Li… - Nutrients, 2023 - mdpi.com
Background and aims: Malnutrition is widely present and influences the prognosis of elderly
inpatients, so it is helpful to be able to identify it with a convenient method. However, in the …

[HTML][HTML] Explainable artificial intelligence-based decision support system for assessing the nutrition-related geriatric syndromes

V Petrauskas, R Jasinevicius, G Damuleviciene… - Applied Sciences, 2021 - mdpi.com
The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis
of a geriatric patient is a complex, experience-based, and time-consuming process that …

[HTML][HTML] Predicting malnutrition from longitudinal patient trajectories with deep learning

BT Jin, MH Choi, MF Moyer, DA Kim - PloS one, 2022 - journals.plos.org
Malnutrition is common, morbid, and often correctable, but subject to missed and delayed
diagnosis. Better screening and prediction could improve clinical, functional, and economic …

Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol

W Ma, B Cai, Y Wang, L Wang, MW Sun, CD Lu… - BMJ open, 2024 - bmjopen.bmj.com
Background Patients with acute abdomen often experience reduced voluntary intake and a
hypermetabolic process, leading to a high occurrence of malnutrition. The Global …

[HTML][HTML] Artificial Intelligence in Malnutrition: A systematic literature review

SMW Janssen, Y Bouzembrak, B Tekinerdogan - Advances in Nutrition, 2024 - Elsevier
Malnutrition among the population of the world is a frequent yet underdiagnosed problem in
both children and adults. Development of malnutrition screening and diagnostic tools for …

[HTML][HTML] Protocol: Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research …

W Ma, B Cai, Y Wang, L Wang, MW Sun, CD Lu… - BMJ Open, 2024 - ncbi.nlm.nih.gov
Background Patients with acute abdomen often experience reduced voluntary intake and a
hypermetabolic process, leading to a high occurrence of malnutrition. The Global …