Health Guardian - A inteligência artificial a serviço do tratamento das doenças tropicais negligenciadas
Resumo
Doenças como malária, arbovírus, tuberculose e hanseníase, conhecidas como doenças tropicais negligenciadas, representam uma ameaça à saúde das populações de baixa renda, impactando negativamente a qualidade de vida dos indivíduos afetados. Este artigo apresenta o Health Guardian, um aplicativo colaborativo que utiliza inteligência artificial para auxiliar os profissionais de saúde no processo de tomada de decisão referente ao tratamento de doenças tropicais negligenciadas, visando melhorar a eficiência do atendimento e a qualidade de vida dos pacientes.
Palavras-chave:
doenças tropicais negligenciadas, inteligência artificial, aplicativo móvel
Referências
Barboza, M. F. X., de Carvalho Monteiro, K. H., Dourado, R. A., Sampaio, V. S., and Endo, P. T. (2023). Enhancing malaria prediction in amazonas, brazil: A comparative analysis of deep learning models based on clustering.
Da Silva, M. H. L. F., Sampaio, V. D. S., Endo, P. T., et al. (2023). Machine-learning model for classification of the prognosis of tuberculosis using real data from brazil. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6. IEEE.
Da Silva Neto, S. R., Tabosa, T., Medeiros Neto, L., Teixeira, I. V., Sadok, S., De Souza Sampaio, V., and Endo, P. T. (2023). Binary models for arboviruses classification using machine learning: A benchmarking evaluation.
de Castañeda, R. R., Durso, A. M., Ray, N., Fernández, J. L., Williams, D. J., Alcoba, G., Chappuis, F., Salathe, M., and Bolon, I. (2019). Snakebite and snake identification: empowering neglected communities and health-care providers with ai. The Lancet Digital Health, 1(5):e202–e203.
Goyal, M., Knackstedt, T., Yan, S., and Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in biology and medicine, 127:104065.
Hotez, P. J. (2021). Forgotten people, forgotten diseases: the neglected tropical diseases and their impact on global health and development. John Wiley & Sons.
Lino Ferreira da Silva Barros, M. H., Oliveira Alves, G., Morais Florencio Souza, L., da Silva Rocha, E., Lorenzato de Oliveira, J. F., Lynn, T., Sampaio, V., and Endo, P. T. (2021). Benchmarking machine learning models to assist in the prognosis of tuberculosis. In Informatics, volume 8, page 27. MDPI.
Sharma, A. K., Nandal, A., Dhaka, A., and Dixit, R. (2021). Medical image classification techniques and analysis using deep learning networks: a review. Health informatics: a computational perspective in healthcare, pages 233–258.
Tabosa de Oliveira, T., da Silva Neto, S. R., Teixeira, I. V., Aguiar de Oliveira, S. B., de Almeida Rodrigues, M. G., Sampaio, V. S., and Endo, P. T. (2022). A comparative study of machine learning techniques for multi-class classification of arboviral diseases. Frontiers in Tropical Diseases, 2:769968.
Zafar, A., Attia, Z., Tesfaye, M., Walelign, S., Wordofa, M., Abera, D., Desta, K., Tsegaye, A., Ay, A., and Taye, B. (2022). Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data. PLOS Neglected Tropical Diseases, 16(6):e0010517.
Da Silva, M. H. L. F., Sampaio, V. D. S., Endo, P. T., et al. (2023). Machine-learning model for classification of the prognosis of tuberculosis using real data from brazil. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6. IEEE.
Da Silva Neto, S. R., Tabosa, T., Medeiros Neto, L., Teixeira, I. V., Sadok, S., De Souza Sampaio, V., and Endo, P. T. (2023). Binary models for arboviruses classification using machine learning: A benchmarking evaluation.
de Castañeda, R. R., Durso, A. M., Ray, N., Fernández, J. L., Williams, D. J., Alcoba, G., Chappuis, F., Salathe, M., and Bolon, I. (2019). Snakebite and snake identification: empowering neglected communities and health-care providers with ai. The Lancet Digital Health, 1(5):e202–e203.
Goyal, M., Knackstedt, T., Yan, S., and Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in biology and medicine, 127:104065.
Hotez, P. J. (2021). Forgotten people, forgotten diseases: the neglected tropical diseases and their impact on global health and development. John Wiley & Sons.
Lino Ferreira da Silva Barros, M. H., Oliveira Alves, G., Morais Florencio Souza, L., da Silva Rocha, E., Lorenzato de Oliveira, J. F., Lynn, T., Sampaio, V., and Endo, P. T. (2021). Benchmarking machine learning models to assist in the prognosis of tuberculosis. In Informatics, volume 8, page 27. MDPI.
Sharma, A. K., Nandal, A., Dhaka, A., and Dixit, R. (2021). Medical image classification techniques and analysis using deep learning networks: a review. Health informatics: a computational perspective in healthcare, pages 233–258.
Tabosa de Oliveira, T., da Silva Neto, S. R., Teixeira, I. V., Aguiar de Oliveira, S. B., de Almeida Rodrigues, M. G., Sampaio, V. S., and Endo, P. T. (2022). A comparative study of machine learning techniques for multi-class classification of arboviral diseases. Frontiers in Tropical Diseases, 2:769968.
Zafar, A., Attia, Z., Tesfaye, M., Walelign, S., Wordofa, M., Abera, D., Desta, K., Tsegaye, A., Ay, A., and Taye, B. (2022). Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data. PLOS Neglected Tropical Diseases, 16(6):e0010517.
Publicado
29/04/2024
Como Citar
ANDRADE, Hilson G. Vilar de; BARROS, Maicon Herverton Lino Ferreira da Silva; MELLO, Maria Eduarda Ferro de; ENDO, Patricia Takako.
Health Guardian - A inteligência artificial a serviço do tratamento das doenças tropicais negligenciadas. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 19. , 2024, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 151-156.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2024.238051.
