Recognizing and Predicting the Risk of Malnutrition in the Elderly Using Artificial Intelligence: A Systematic Review
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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Bond, A., Mccay, K., & Lal, S. (2023). Artificial intelligence & clinical nutrition: What the future might have in store. Clinical nutrition ESPEN, 57, 542-549. https://doi.org/10.1016/j.clnesp.2023.07.082
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Cereda, E. (2012). Mini nutritional assessment. Curr Opin Clin Nutr Metab Care, 15, 29–41. https://doi.org/10.1097/MCO.0b013e32834d7647
Cioara, T., Anghel, I., Salomie, I., Barakat, L., Miles, S., Reidlinger, D., ... & Pop, F. (2018). Expert system for nutrition care process of older adults. Future Generation Computer Systems, 80, 368-383. https://doi.org/10.1016/j.future.2017.05.037
Corkins, M. R., Guenter, P., DiMaria‐Ghalili, R. A., Jensen, G. L., Malone, A., Miller, S., ... & American Society for Parenteral and Enteral Nutrition. (2014). Malnutrition diagnoses in hospitalized patients: United States, 2010. Journal of Parenteral and Enteral Nutrition, 38(2), 186-195. https://doi.org/10.1177/0148607113512154
Correia, M. I. T., & Waitzberg, D. L. (2003). The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clinical nutrition, 22(3), 235-239. https://doi.org/10.1016/S0261-5614(02)00215-7
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de Nutrición Parenteral, S. S. E. (2008). Valoración Nutricional en el anciano: recomendaciones prácticas de los expertos en geriatría y nutrición.
De Van Der Schueren, M., Keller, H., Cederholm, T., Barazzoni, R., Compher, C., Correia, M. I. T. D., ... & GLIM Consortium. (2020). Global Leadership Initiative on Malnutrition (GLIM): Guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. Clinical nutrition, 39(9), 2872-2880. https://doi.org/10.1016/j.clnu.2019.12.022
Dent, E., Wright, O. R., Woo, J., & Hoogendijk, E. O. (2023). Malnutrition in older adults. The Lancet, 401(10380), 951-966. https://doi.org/10.1016/s0140-6736(22)02612-5
Drewnowski, A., & Shultz, J. M. (2001). Impact of aging on eating behaviors, food choices, nutrition, and health status. The journal of nutrition, health & aging, 5(2), 75-79. Impact of aging on eating behaviors, food choices, nutrition, and health status - PubMed (nih.gov)
Elfert, P., Tiryaki, E., Eichelberg, M., Rösch, N., & Hein, A. (2021, September). A deep learning assisted digital nutrition diary to support nutrition counseling for people affected by the geriatric frailty syndrome. In 2021 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-7). IEEE. http://dx.doi.org/10.1109/ISCC53001.2021.9631394
Fávaro-Moreira, N. C., Krausch-Hofmann, S., Matthys, C., Vereecken, C., Vanhauwaert, E., Declercq, A., ... & Duyck, J. (2016). Risk factors for malnutrition in older adults: a systematic review of the literature based on longitudinal data. Advances in nutrition, 7(3), 507-522.https://doi.org/10.3945/an.115.011254
Ghosh, J.& Ghosh,D., (2021). Nutritional Assessment and Associated Factors in the Elderly: An Overview. Journal of Nutraceuticals and Food Science, 6(4), 21.
Ghosh, J., Chaudhuri, A. N., Saha, I., & Chaudhuri, D. (2021). Antimicrobial Effect of 1, 25 Dihydroxy Vitamin D on Vibrio cholerae and its Association with Serum 25-Hydroxy Vitamin D Level in Rural Elderly Women: An Experimental Study. Journal of Clinical & Diagnostic Research, 15(10), 34-37. https://doi.org/10.7860/JCDR/2021/49439.15571
Ghosh, J., Chaudhuri, D., Saha, I., & Chaudhuri, A. N. (2020). Prevalence of metabolic syndrome, vitamin D level, and their association among elderly women in a rural community of West Bengal, India. Medical Journal of Dr. DY Patil University, 13(4), 315-320. https://doi.org/10.4103/mjdrdypu.mjdrdypu_229_19
Ghosh, J., Chaudhuri, D., Saha, I., & Chaudhuri, A. N. (2022). Association of conicity index with different cardiovascular disease risk factors among rural elderly women of West Bengal, India. Indian Journal of Community Medicine, 47(1), 18-22. https://doi.org/10.4103/ijcm.ijcm_595_21
Ghosh, J., Chaudhuri, D., Saha, I., & Chaudhuri, A. N. (2022). Prevalence of anaemia and its association with metabolic syndrome and its components among rural elderly women of Amdanga block, North 24th PGS West Bengal. Indian Journal of Gerontology, 36(2), 148-158.
Ghosh, J., Choudhury, S. R., Singh, K., & Koner, S. (2024). Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome related complications: A review. International Journal of Advancement in Life Sciences Research, 7(2), 41-67. https://doi.org/10.31632/ijalsr.2024.v07i02.004
Katsas, K., Mamalaki, E., Kontogianni, M. D., Anastasiou, C. A., Kosmidis, M. H., Varlamis, I., ... & Yannakoulia, M. (2020). Malnutrition in older adults: Correlations with social, diet-related, and neuropsychological factors. Nutrition, 71. https://doi.org/10.1016/j.nut.2019.110640
Larburu, N., Artola, G., Kerexeta, J., Caballero, M., Ollo, B., & Lando, C. M. (2022). Key factors and AI-based risk prediction of malnutrition in hospitalized older women. Geriatrics, 7(5), 105. https://doi.org/10.3390/geriatrics7050105
Lengelé, L., Bruyère, O., Beaudart, C., Reginster, J. Y., & Locquet, M. (2021). Impact of Malnutrition Status on Muscle Parameter Changes over a 5-Year Follow-Up of Community-Dwelling Older Adults from the SarcoPhAge Cohort. Nutrients, 13(2), 407. https://doi.org/10.3390/nu13020407
Lu, Y., Stathopoulou, T., Vasiloglou, M. F., Christodoulidis, S., Stanga, Z., & Mougiakakou, S. (2020). An artificial intelligence-based system to assess nutrient intake for hospitalised patients. IEEE transactions on multimedia, 23, 1136-1147. https://doi.org/10.1109/embc.2019.8856889
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. A Unified Approach to Interpreting Model Predictions (nips.cc)
Ma, W., Cai, B., Wang, Y., Wang, L., Sun, M. W., Lu, C. D., & Jiang, H. (2024). Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol. BMJ open, 14(3). https://doi.org/10.1136/bmjopen-2023-077734
O'Keeffe, M., Kelly, M., O'Herlihy, E., O'Toole, P. W., Kearney, P. M., Timmons, S., ... & O'Connor, E. M. (2019). Potentially modifiable determinants of malnutrition in older adults: A systematic review. Clinical Nutrition, 38(6), 2477-2498. https://doi.org/10.1016/j.clnu.2018.12.007
Pang, X., Forrest, C. B., Lê-Scherban, F., & Masino, A. J. (2019, December). Understanding early childhood obesity via interpretation of machine learning model predictions. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 1438-1443). IEEE. https://doi.org/10.1016/j.ijmedinf.2021.104454
Pfisterer, K. J., Amelard, R., Keller, H. H., & Wong, A. (2023). Characterizing Canadian long-term care home consumed foods and their inflammatory potential: a secondary analysis. BMC Public Health, 23(1), 261. https://doi.org/10.1186/s12889-022-14934-8
Roy, M., Gaudreau, P., & Payette, H. (2016). A scoping review of anorexia of aging correlates and their relevance to population health interventions. Appetite, 105, 688-699. https://doi.org/10.1016/j.appet.2016.06.037
Sauer, A. C., Alish, C. J., Strausbaugh, K., West, K., & Quatrara, B. (2016). Nurses needed: Identifying malnutrition in hospitalized older adults. NursingPlus Open, 2, 21-25. https://doi.org/10.1016/j.npls.2016.05.001
Shi, H., Yang, D., Tang, K., Hu, C., Li, L., Zhang, L., ... & Cui, Y. (2022). Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clinical Nutrition, 41(1), 202-210. https://doi.org/10.1016/j.clnu.2021.11.006
Singh, K., Choudhury, S. R., Ghosh, J., Koner, S., & Pal, R. (2023). Ageing and its relationship with psychoneuroimmunology. Journal of Comprehensive Health, 11(2). 34-47. Ageing and its relationship with psychoneuroimmunology - Journal of Comprehensive Health
Teigen, L. M., Kuchnia, A. J., Nagel, E. M., Price, K. L., Hurt, R. T., & Earthman, C. P. (2018). Diagnosing clinical malnutrition: Perspectives from the past and implications for the future. Clinical nutrition ESPEN, 26, 13-20. https://doi.org/10.1016/j.clnesp.2018.05.006
Torres, J., Artola, G., Larburu, N., Agirre, A., Narbaiza, E., Berges González, I., & Lizaso, A. (2022). Personalized nutritional guidance system to prevent malnutrition in pluripathological older patients. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health—ICT4AWE 2022, 338–343. http://hdl.handle.net/10810/63353
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How to Cite
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
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Review Articles
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