Recognizing and Predicting the Risk of Malnutrition in the Elderly Using Artificial Intelligence: A Systematic Review

  • Joyeta Ghosh 1Department of Dietetics and Applied Nutrition, Amity University Kolkata, Kolkata, India 2School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK https://orcid.org/0000-0001-9619-1793

Abstract

The global population is aging at an alarming rate, endangering conventional care models that have always relied on in-person supervision. Elderly malnutrition has been found to be a serious health problem associated with health deterioration, which has several, immediate effects on everyday activities and standard of living. Deficiency in nutrients is a frequent and serious concern in older adults and might have an impact on the emergence of geriatric diseases. The two most obvious indicators of malnutrition in the elderly include unintentional weight loss and a lower value of the body mass index (BMI). However, non-symptomatic or unnoticed insufficiencies, such as lower than expected level of different crucial micronutrients, which are quite challenging to diagnose and are typically disregarded among older residents of their communities. Artificial intelligence (AI) permeates every aspect of human existence. The current systematic review looks at how AI-based technology is currently being used and how it affects elderly malnutrition. Using the following keywords, computerized literature searches of many reliable data bases were used to compile relevant published articles: malnutrition, artificial intelligence (AI), machine learning (ML), elderly nutrition, risk factors of malnutrition among the elderly, chronic diseases, elderly. Prospective studies with original data were chosen, and their significant findings were integrated into the analysis of the state of malnutrition at the moment. In conclusion, it is challenging to deploy AI-based food and nutrient intake monitoring data because no single program is suitable for all international cuisines and eating customs. Geographic variations in the dietary habits of the population make it challenging to compile the necessary data sets for deep learning. Furthermore, even within a same region, hospital menus differ from patient to patient. It is suggested that meals served in hospitals under the same management be standardized to facilitate the procedure.

Keywords: Ageing, Artificial Intelligence, Machine learning, Malnutrition

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References

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Ghosh, J. (2024). Recognizing and Predicting the Risk of Malnutrition in the Elderly Using Artificial Intelligence: A Systematic Review. International Journal of Advancement in Life Sciences Research, 7(3), 1-14. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i03.001