Reconhecimento de Padrões em Atendimentos do SUS no RS
Resumo
A escassez de recursos financeiros e tecnológicos desafia os gestores na otimização da gestão dos recursos da saúde pública. O grande volume de dados torna complexo gerir e monitorar esses recursos. Contribuindo para a inovação tecnológica na saúde pública no Rio Grande do Sul (RS, Brasil), este trabalho propõe o uso de técnicas de inteligência artificial para reconhecer padrões e detectar anomalias nos atendimentos SUS do programa ASSISTIR.Referências
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Campello, R. J. G. B., Moulavi, D., and Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pei, J., Tseng, V. S., Cao, L., Motoda, H., and Xu, G., editors, Advances in Knowledge Discovery and Data Mining, pages 160–172, Berlin. Springer.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226–231.
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Mellouli, S., Janssen, M., and Ojo, A. (2024). Introduction to the issue on artificial intelligence in the public sector: Risks and benefits of AI for governments. Digit. Gov. Res. Pract., 5(1):1–6.
Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science, pages 195–211. Springer International Publishing, Cham.
Pasin, O. e Gonenc, S. (2023). An investigation into epidemiological situations of COVID-19 with fuzzy k-means and k-prototype clustering methods. Sci. Rep., 13(1):6255.
Campello, R. J. G. B., Moulavi, D., and Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pei, J., Tseng, V. S., Cao, L., Motoda, H., and Xu, G., editors, Advances in Knowledge Discovery and Data Mining, pages 160–172, Berlin. Springer.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226–231.
Liu, F. T., Ting, K. M., and Zhou, Z. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining. IEEE.
Mellouli, S., Janssen, M., and Ojo, A. (2024). Introduction to the issue on artificial intelligence in the public sector: Risks and benefits of AI for governments. Digit. Gov. Res. Pract., 5(1):1–6.
Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science, pages 195–211. Springer International Publishing, Cham.
Pasin, O. e Gonenc, S. (2023). An investigation into epidemiological situations of COVID-19 with fuzzy k-means and k-prototype clustering methods. Sci. Rep., 13(1):6255.
Publicado
12/11/2025
Como Citar
DORNELES, Carina R. S.; BECKER, Karin; COMBA, João L. D.; FREITAS, Carla M. D. S.
Reconhecimento de Padrões em Atendimentos do SUS no RS. In: ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL DA REGIÃO SUL (ERAMIA-RS), 1. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 136-139.
DOI: https://doi.org/10.5753/eramiars.2025.16721.
