Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning (2024)
- Authors:
- USP affiliated authors: CHIAVEGATTO FILHO, ALEXANDRE DIAS PORTO - FSP ; BARCELLOS FILHO, FABIANO NOVAES - FSP
- Unidade: FSP
- DOI: 10.1371/journal.pdig.0000699
- Subjects: COVID-19; APRENDIZADO COMPUTACIONAL; ESTUDOS MULTICÊNTRICOS; PREDIÇÃO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: San Francisco California
- Date published: 2024
- Source:
- Título: PLOS Digital Health
- ISSN: 2767-3170
- Volume/Número/Paginação/Ano: v.3, n.12, art. e0000699. [13p.], 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
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: 20 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.0000699 -
NLM
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. 20 ] Available from: https://doi.org/10.1371/journal.pdig.0000699 -
Vancouver
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. 20 ] Available from: https://doi.org/10.1371/journal.pdig.0000699 - Artificial intelligence for the diagnosis of erythematous-squamous dermatological diseases: technological contributions to primary care
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Informações sobre o DOI: 10.1371/journal.pdig.0000699 (Fonte: oaDOI API)
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