Predicting special care during the COVID-19 pandemic: a machine learning approach

Author:

Bezzan Vitor P.,Rocco Cleber D.ORCID

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Reference53 articles.

1. Alsheref FK, Gomaa WH. Blood diseases detection using classical machine learning algorithms. Int J Adv Comput Sci Appl. 2019;10(9):58–79.

2. Bakshy E, Dworkin L, et al. Ae: a main-agnostic platform for adaptive experimentation. In: NIPS’18: Proceedings of the 31th international conference on neural information processing systems 2018.

3. Beeler C, Dbeibo L, et al. Assessing patient risk of central line-associated bacteremia via machine learning. Am J Infect Control. 2018;46(9):986–91.

4. Bertsimas D, O'Hair AK, Pulleyblank WR. The analytics edge. Belmont: Dynamic Ideas LLC; 2015.

5. Bonanad C, García-Blas S, et al. The effect of age on mortality inpatients with covid-19: A meta-analysis with 611.583 subjects. J Am Med Direct Assoc. 2020;21:915–8.

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