DoN-DataOnNutrition®: A platform for managing nutritional therapy in critical patients and supporting ICU research
Abstract
Nutritional therapy (NT) in critically ill patients remains a challenge. Hospital malnutrition worsens the condition and negatively impacts patients' health. Information technology (IT) and artificial intelligence (AI) offer promising solutions to optimize NT. The DoN-DataOnNutriton® system integrates clinical and laboratory data, providing real-time recommendations and alerts for risks, non-conformities, and changes in tests. The system's structured database, maintained securely and privately in the cloud, facilitates research and the development of personalized solutions to improve precision nutritional therapy.References
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Boehm, B. (1988) A Spiral Model of Software Development”. IEEE Computer, v. 21, n. 5, p. 61-72.
Collins, K and Huen, S. C. (2023) Metabolism and Nutrition in Sepsis: In Need of a Paradigm Shift, Nephron, vol. 147, no. 12, pp. 733–736. DOI: 10.1159/000534074.
Correia, M. I. T. D.; et al. (2019) Nutritional status of patients at hospital admission in Brazil”, The American Journal of Clinical Nutrition, v. 110, n. 3, p. 605-614.
EL-Manzalawy, Y. et al. OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality, (2021) BMC Medical Informatics and Decision Making, vol. 21, no. 1, p. 156. DOI: 10.1186/s12911-021-01517-7.
Johnson, A. E. W. et al. (2023) MIMIC-IV, a freely accessible electronic health record dataset, Scientific Data, vol. 10, no. 1, p. 1. DOI: 10.1038/s41597-022-01899-x.
Lambell, K. J.; et al. (2020) Nutrition therapy in critical illness: a review of the literature for clinicians, Critical Care, vol. 24, no. 1, p. 35. DOI: 10.1186/s13054-020-2739-4.
Mcclave, S. A. et al. (2016) Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Adult Critically Ill Patient, Journal of Parenteral and Enteral Nutrition, vol. 40, no. 2, pp. 159–211. DOI: 10.1177/0148607115621863.
Oliveira, C. A. and Gaidzinski, R. R. (2017) Applicability of the Clinical Nutrition Interventions Classification and Activities in a Hospital Unit. Pilot Study, Cienc Cuid Saude, 16(4):1-7.
Raslan, L. I. et al. (2011) Desnutrição hospitalar: subdiagnóstico e suas implicações, Revista Brasileira de Nutrição Clínica, v. 26, n. 1, p. 22-27.
Singer, P., Robinson, E. and Raphaeli, O. (2023) Gastrointestinal failure, big data and intensive care, Current Opinion in Clinical Nutrition & Metabolic Care, vol. 26, no. 5, pp. 476–481. DOI: 10.1097/MCO.0000000000000961.
Sutherland, J. (2014) Scrum: The Art of Doing Twice the Work in Half the Time. Crown Business.
Waitzberg, D. L.; et al. (2001) Inquérito multicêntrico de avaliação nutricional em hospitais da América Latina: Estudo IBRANUTRI, Nutrição em Pauta, v. 9, n. 47, p. 24-34.
Published
2025-06-09
How to Cite
SANTOS, Trícia S. et al.
DoN-DataOnNutrition®: A platform for managing nutritional therapy in critical patients and supporting ICU research. In: TOOLS AND APPLICATIONS - BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 223-228.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2025.7094.
