Investigação de Padrões Epidemiológicos em Oncologia Pediátrica a partir de Integração e Visualização de Dados
Resumo
Este artigo descreve um estudo com o objetivo de investigar padrões epidemiológicos, nas bases de dados do DATASUS e RHC, a partir da extração e processamento de dados relacionados ao câncer infantojuvenil. A partir dos dados coletados, tratados e integrados, foi desenvolvido um sistema web composto por três dashboards, os quais apresentam diferentes tipos de representações visuais interativas de dados de oncologia pediátrica. O sistema está disponibilizado para acesso público, uma vez que as informações coletadas das bases de dados citadas já estão anonimizadas, e visa auxiliar, principalmente, a tomada de decisão estratégica relacionada ao tratamento e prevenção do câncer infantil.Referências
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Batko, K. and Ślezak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1):3.
Bernardi, F. A., Alves, D., Crepaldi, N. Y., Yamada, D. B., Lima, V. C., and Lopes Rijo, R. P. C. (2022). Data quality in health research: a systematic literature review. medRxiv.
Chan, T., Sebok-Syer, S., Thoma, B., Wise, A., Sherbino, J., and Pusic, M. (2018). Learning analytics in medical education assessment: The past, the present, and the future. AEM Education and Training, 2(2):178–187.
Costantini, A., Michels, F., Ruhl, J., Hill, S., Kohler, B., and Negoita, S. (2023). The trajectory of pediatric cancer data and collection in the united states. Journal of Registry Management, 50(3):82–84.
Gazzarata, R., Almeida, J., Lindsköld, L., Cangioli, G., Gaeta, E., Fico, G., and Chronaki, C. E. (2024). Hl7 fast healthcare interoperability resources (hl7 fhir) in digital healthcare ecosystems for chronic disease management: Scoping review. International Journal of Medical Informatics, 189:105507.
INCA (2023). Estimativa 2023: incidência de câncer no Brasil. Instituto Nacional de Câncer José Alencar Gomes da Silva. Coordenação de Prevenção e Vigilância, Rio de Janeiro.
Jensen, P. B., Jensen, L. J., and Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6):395–405.
Khan, S. S. and Qin, G. (2018). A review on healthcare data analytics: Recent advances and future directions. Journal of King Saud University-Computer and Information Sciences.
Linet, M. S., Ries, L. A. G., Smith, M. A., Tarone, R. E., and Devesa, S. S. (1999). Cancer surveillance series: Recent trends in childhood cancer incidence and mortality in the united states. JNCI: Journal of the National Cancer Institute, 91(12):1051–1058.
Mohammadzadeh, Z., Ghazisaeedi, M., Nahvijou, A., Rostam Niakan Kalhori, S., Davoodi, S., and Zendehdel, K. (2017). Systematic review of hospital based cancer registries (hbcrs): Necessary tool to improve quality of care in cancer patients. Asian Pacific Journal of Cancer Prevention, 18(8):2027–2033.
Obermeyer, Z. and Emanuel, E. J. (2016). Predicting the future - big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13):1216–1219.
Osterman, T. J., Terry, M., and Miller, R. S. (2020). Improving cancer data interoperability: The promise of the minimal common oncology data elements (mcode) initiative. JCO Clinical Cancer Informatics, (4):993–1001. PMID: 33136433.
Park, S., Bekemeier, B., Flaxman, A., and Schultz, M. (2022). Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review. Informatics for Health and Social Care, 47(2):175–193.
Rodriguez-Galindo, C., Friedrich, P., Morrissey, L., and Frazier, L. (2013). Global challenges in pediatric oncology. Current Opinion in Pediatrics, 25(1):3–15.
Batko, K. and Ślezak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1):3.
Bernardi, F. A., Alves, D., Crepaldi, N. Y., Yamada, D. B., Lima, V. C., and Lopes Rijo, R. P. C. (2022). Data quality in health research: a systematic literature review. medRxiv.
Chan, T., Sebok-Syer, S., Thoma, B., Wise, A., Sherbino, J., and Pusic, M. (2018). Learning analytics in medical education assessment: The past, the present, and the future. AEM Education and Training, 2(2):178–187.
Costantini, A., Michels, F., Ruhl, J., Hill, S., Kohler, B., and Negoita, S. (2023). The trajectory of pediatric cancer data and collection in the united states. Journal of Registry Management, 50(3):82–84.
Gazzarata, R., Almeida, J., Lindsköld, L., Cangioli, G., Gaeta, E., Fico, G., and Chronaki, C. E. (2024). Hl7 fast healthcare interoperability resources (hl7 fhir) in digital healthcare ecosystems for chronic disease management: Scoping review. International Journal of Medical Informatics, 189:105507.
INCA (2023). Estimativa 2023: incidência de câncer no Brasil. Instituto Nacional de Câncer José Alencar Gomes da Silva. Coordenação de Prevenção e Vigilância, Rio de Janeiro.
Jensen, P. B., Jensen, L. J., and Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6):395–405.
Khan, S. S. and Qin, G. (2018). A review on healthcare data analytics: Recent advances and future directions. Journal of King Saud University-Computer and Information Sciences.
Linet, M. S., Ries, L. A. G., Smith, M. A., Tarone, R. E., and Devesa, S. S. (1999). Cancer surveillance series: Recent trends in childhood cancer incidence and mortality in the united states. JNCI: Journal of the National Cancer Institute, 91(12):1051–1058.
Mohammadzadeh, Z., Ghazisaeedi, M., Nahvijou, A., Rostam Niakan Kalhori, S., Davoodi, S., and Zendehdel, K. (2017). Systematic review of hospital based cancer registries (hbcrs): Necessary tool to improve quality of care in cancer patients. Asian Pacific Journal of Cancer Prevention, 18(8):2027–2033.
Obermeyer, Z. and Emanuel, E. J. (2016). Predicting the future - big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13):1216–1219.
Osterman, T. J., Terry, M., and Miller, R. S. (2020). Improving cancer data interoperability: The promise of the minimal common oncology data elements (mcode) initiative. JCO Clinical Cancer Informatics, (4):993–1001. PMID: 33136433.
Park, S., Bekemeier, B., Flaxman, A., and Schultz, M. (2022). Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review. Informatics for Health and Social Care, 47(2):175–193.
Rodriguez-Galindo, C., Friedrich, P., Morrissey, L., and Frazier, L. (2013). Global challenges in pediatric oncology. Current Opinion in Pediatrics, 25(1):3–15.
Publicado
09/06/2025
Como Citar
RIBEIRO, Felipe de Assis; SINIGAGLIA, Marialva; ÁVILA, Alexsandro Vargas de; SILVA, Isabel Cristina Siqueira da.
Investigação de Padrões Epidemiológicos em Oncologia Pediátrica a partir de Integração e Visualização de Dados. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 677-688.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7713.
