Solução Multiagente para Prescrição de Diálise Peritoneal
Resumo
A doença renal crônica, em seus estágios avançados, frequentemente requer a implementação de terapias de substituição renal (TSR) para realizar as funções excretoras comprometidas dos rins. Entre as modalidades de TSR, a diálise peritoneal (DP) se destaca por sua flexibilidade e custo mais baixo. Sua eficácia depende crucialmente da adequação do tratamento às condições únicas do paciente. Historicamente, a prescrição de DP é um processo bastante iterativo, que emprega princípios heurísticos e avaliações padronizadas. Este Artigo propõe o desenvolvimento e validação de uma Solução Multiagente para prescrição de regimes de DP, a fim de otimizar tempo e recursos, além de melhorar a qualidade de vida dos pacientes.Referências
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Grassmann A, Gioberge S, Moeller S, et al. (2005) “ESRD patients in 2004: global overview of patient numbers, treatment modalities and associated trends”. Nephrology Dialysis Transplantation, v. 20, i. 12, p. 2587–2593.
Galli, E.G., Taietti, C. e Borghi, M. (2011) “Personalization of Automated Peritoneal Dialysis Treatment Using a Computer Modeling System”, Advances in Peritoneal Dialysis, Vol. 27, 90–96, 2011.
Milan Manani, S., Crepaldi, C., Giuliani, A., Virzì, G. M., Garzotto, F., Riello, C., ... & Ronco, C. (2018) “Remote monitoring of automated peritoneal dialysis improves personalization of dialytic prescription and patient’s independence”, In: Blood Purification, 46(2), 111-117.
John, O., & Jha, V. (2019) “Remote patient management in peritoneal dialysis: an answer to an unmet clinical need”. Remote patient management in peritoneal dialysis, 197, 99-112.
American Journal of Kidney Diseases. (2006) “Clinical Practice Guidelines for
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Lindholm, B.; Krediet, R. T. (2009) “Peritoneal Dialysis: From Basic Concepts to Clinical Excellence”. In: Contributions to Nephrology. Basel: Karger, vol. 163.
Chakraborty, C.; Bhattacharya, M.; Pal, S.; Lee, S.-S. (2024) “From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare”. Current Research in Biotechnology, v. 7. ISSN 2590-2628.
Aragão Pereira, J.P. Franco Brandão, A.A. Bevilacqua, J.d.S. Côrrea-Giannella, M.L.C. (2022) “A Multi-Agent Approach Used to Predict Long-Term Glucose Oscillation in Individuals with Type 1 Diabetes”. Appl. Sci. 2022, 12, 9641. DOI: 10.3390/app12199641
M. Zhang, Y. Hu, and T. Wang. (2005) “Selection of peritoneal dialysis schemes based on multi-objective fuzzy pattern recognition”. In: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol. 22, no. 2, pp. 335–338, 2005
Alexandru Burlacu A., Adrian Iftene, Daniel Jugrin, Iolanda Valentina Popa, Paula Madalina Lupu, Cristiana Vlad, Adrian Covic. (2020) “Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review”. In: BioMed Research International, Volume 2020, Article ID 9867872, disponível em DOI: 10.1155/2020/9867872
NKUDIC, NIDDKD and NIH. (2024) “dialys.gif”. In: National Kidney and Urologic Diseases Information Clearinghouse, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, USA - [link], Public Domain, [link], 2024
Jameson, J.; Fauci, A. S.; Kasper, D. L.; Hauser, S. L.; Longo, D. L.; Loscalzo, J (2018) “Acute Kidney Injury”. In: Jameson, J.; Fauci, A. S.; Kasper, D. L.; Hauser, S. L.; Longo, D. L.; Loscalzo, J. (Eds.). Harrison's Principles of Internal Medicine. 20th ed. New York: McGraw-Hill Education, 2018. p. 2099-2111.
Russell, J. S.; Norvig, P. (2010) “Artificial Intelligence A Modern Approach”, Pearson Education, 3ª edição.
Python. (2024) Disponível em: [link]
SPADE. Smart Python Agent Development Environment (2020) Disponível em: [link]
Grassmann A, Gioberge S, Moeller S, et al. (2005) “ESRD patients in 2004: global overview of patient numbers, treatment modalities and associated trends”. Nephrology Dialysis Transplantation, v. 20, i. 12, p. 2587–2593.
Galli, E.G., Taietti, C. e Borghi, M. (2011) “Personalization of Automated Peritoneal Dialysis Treatment Using a Computer Modeling System”, Advances in Peritoneal Dialysis, Vol. 27, 90–96, 2011.
Milan Manani, S., Crepaldi, C., Giuliani, A., Virzì, G. M., Garzotto, F., Riello, C., ... & Ronco, C. (2018) “Remote monitoring of automated peritoneal dialysis improves personalization of dialytic prescription and patient’s independence”, In: Blood Purification, 46(2), 111-117.
John, O., & Jha, V. (2019) “Remote patient management in peritoneal dialysis: an answer to an unmet clinical need”. Remote patient management in peritoneal dialysis, 197, 99-112.
American Journal of Kidney Diseases. (2006) “Clinical Practice Guidelines for
Peritoneal Dialysis Adequacy”,Volume 48, Suplemento 1, 98-129,
Lindholm, B.; Krediet, R. T. (2009) “Peritoneal Dialysis: From Basic Concepts to Clinical Excellence”. In: Contributions to Nephrology. Basel: Karger, vol. 163.
Chakraborty, C.; Bhattacharya, M.; Pal, S.; Lee, S.-S. (2024) “From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare”. Current Research in Biotechnology, v. 7. ISSN 2590-2628.
Aragão Pereira, J.P. Franco Brandão, A.A. Bevilacqua, J.d.S. Côrrea-Giannella, M.L.C. (2022) “A Multi-Agent Approach Used to Predict Long-Term Glucose Oscillation in Individuals with Type 1 Diabetes”. Appl. Sci. 2022, 12, 9641. DOI: 10.3390/app12199641
M. Zhang, Y. Hu, and T. Wang. (2005) “Selection of peritoneal dialysis schemes based on multi-objective fuzzy pattern recognition”. In: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol. 22, no. 2, pp. 335–338, 2005
Alexandru Burlacu A., Adrian Iftene, Daniel Jugrin, Iolanda Valentina Popa, Paula Madalina Lupu, Cristiana Vlad, Adrian Covic. (2020) “Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review”. In: BioMed Research International, Volume 2020, Article ID 9867872, disponível em DOI: 10.1155/2020/9867872
NKUDIC, NIDDKD and NIH. (2024) “dialys.gif”. In: National Kidney and Urologic Diseases Information Clearinghouse, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, USA - [link], Public Domain, [link], 2024
Jameson, J.; Fauci, A. S.; Kasper, D. L.; Hauser, S. L.; Longo, D. L.; Loscalzo, J (2018) “Acute Kidney Injury”. In: Jameson, J.; Fauci, A. S.; Kasper, D. L.; Hauser, S. L.; Longo, D. L.; Loscalzo, J. (Eds.). Harrison's Principles of Internal Medicine. 20th ed. New York: McGraw-Hill Education, 2018. p. 2099-2111.
Russell, J. S.; Norvig, P. (2010) “Artificial Intelligence A Modern Approach”, Pearson Education, 3ª edição.
Python. (2024) Disponível em: [link]
SPADE. Smart Python Agent Development Environment (2020) Disponível em: [link]
Publicado
14/08/2024
Como Citar
COSTA, Augusto Vaccarelli; COELHO, Fernando Falquetto; TAVARES, Lucas Alexandre; BRANDÃO, Anarosa Alves Franco; PASSOS, Rogério da Hora; COSTA, Maristela Carvalho da.
Solução Multiagente para Prescrição de Diálise Peritoneal. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 18. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 168-173.
ISSN 2326-5434.
