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English Information

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Authors
# Name
1 José Figuerêdo(jslfigueredo@ecomp.uefs.br)
2 Renata Araujo-Calumby(farm.renata@hotmail.com)
3 Rodrigo Calumby(rtcalumby@ecomp.uefs.br)

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Reference
# Reference
1 Figuerêdo, J. et al. Machine learning for prognosis of patients with covid-19: An early days analysis. In Anais do XVIII ENIAC. SBC, Porto Alegre, RS, Brasil, pp. 59–70, 2021.
2 Kumar, A. et al. A review of modern technologies for tackling covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 (4): 569 – 573, 2020.
3 Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet 395 (10224): 565–574, 2020.
4 Mattos, J. et al. Clinical risk factors of icu & fatal covid-19 cases in brazil. In Anais do VIII KDMiLe. SBC, Porto Alegre, RS, Brasil, pp. 33–40, 2020.
5 Mittelstadt, B. et al. Explaining explanations in ai. In Proceedings of the Conference on FAT. ACM, New York, NY, USA, pp. 279–288, 2019.
6 Pan, D. et al. A predicting nomogram for mortality in patients with covid-19. Frontiers in Public Health vol. 8, pp. 461, 2020.
7 Ribeiro, M. T. et al. “why should i trust you”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD. ACM, New York, USA, pp. 1135–1144, 2016.
8 Soares, F. et al. Analysis and prediction of childhood pneumonia deaths using machine learning algorithms. In Anais do IX KDMiLe. SBC, Porto Alegre, RS, Brasil, pp. 16–23, 2021.
9 Yan, L. et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2 (5): 283–288, may, 2020.
10 Yu, K.-H. et al. Artificial intelligence in healthcare. Nature Biomedical Engineering 2 (10): 719–731, Oct, 2018.