Savalli, Carine 
; Wichmann, Roberta Moreira ; Barcellos Filho, Fabiano; Fernandes, Fernando Timoteo ; Chiavegatto Filho, Alexandre Dias Porto
Source: PLOS Digital Health. Unidade: FSP
Subjects: COVID-19, APRENDIZADO COMPUTACIONAL, ESTUDOS MULTICÊNTRICOS, PREDIÇÃO
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SAVALLI, Carine et al. Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning. PLOS Digital Health, v. 3, n. 12, p. art. e0000699. [13], 2024Tradução . . Disponível em: https://doi.org/10.1371/journal.pdig.0000699. Acesso em: 07 jan. 2026.APA
Savalli, C., Wichmann, R. M., Barcellos Filho, F., Fernandes, F. T., & Chiavegatto Filho, A. D. P. (2024). Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning. PLOS Digital Health, 3( 12), art. e0000699. [13]. doi:10.1371/journal.pdig.0000699NLM
Savalli C, Wichmann RM, Barcellos Filho F, Fernandes FT, Chiavegatto Filho ADP. Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning [Internet]. PLOS Digital Health. 2024 ;3( 12): art. e0000699. [13].[citado 2026 jan. 07 ] Available from: https://doi.org/10.1371/journal.pdig.0000699Vancouver
Savalli C, Wichmann RM, Barcellos Filho F, Fernandes FT, Chiavegatto Filho ADP. Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning [Internet]. PLOS Digital Health. 2024 ;3( 12): art. e0000699. [13].[citado 2026 jan. 07 ] Available from: https://doi.org/10.1371/journal.pdig.0000699
