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Filtros : "APRENDIZADO COMPUTACIONAL" "Carvalho, Werther Brunow de" Removidos: "Couto, Saulo Brasil do" "Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)" Limpar

Filtros

Artigo de periodico
Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms (2023)
Matsushita, Felipe Yu ; Krebs, Vera Lucia Jornada ; Carvalho, Werther Brunow de
Source: Clinics. Unidade: FM
Subjects: APRENDIZADO COMPUTACIONAL, NEONATOLOGIA, SEPSE, BACTEREMIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATSUSHITA, Felipe Yu e KREBS, Vera Lucia Jornada e CARVALHO, Werther Brunow de. Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms. Clinics, v. 78, 2023Tradução . . Disponível em: https://observatorio.fm.usp.br/handle/OPI/53029. Acesso em: 13 nov. 2024.
APA
Matsushita, F. Y., Krebs, V. L. J., & Carvalho, W. B. de. (2023). Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms. Clinics, 78. doi:10.1016/j.clinsp.2022.100148
NLM
Matsushita FY, Krebs VLJ, Carvalho WB de. Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms [Internet]. Clinics. 2023 ; 78[citado 2024 nov. 13 ] Available from: https://observatorio.fm.usp.br/handle/OPI/53029
Vancouver
Matsushita FY, Krebs VLJ, Carvalho WB de. Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms [Internet]. Clinics. 2023 ; 78[citado 2024 nov. 13 ] Available from: https://observatorio.fm.usp.br/handle/OPI/53029
Artigo de periodico
Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach (2022)
Matsushita, Felipe Yu; Krebs, Vera Lucia Jornada; Carvalho, Werther Brunow de
Source: European journal of pediatrics. Unidade: FM
Subjects: RECÉM-NASCIDO DE BAIXO PESO, FENÓTIPOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATSUSHITA, Felipe Yu e KREBS, Vera Lucia Jornada e CARVALHO, Werther Brunow de. Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach. European journal of pediatrics, v. 181, n. 3, p. 1085-1097, 2022Tradução . . Disponível em: https://doi.org/10.1007/s00431-021-04298-3. Acesso em: 13 nov. 2024.
APA
Matsushita, F. Y., Krebs, V. L. J., & Carvalho, W. B. de. (2022). Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach. European journal of pediatrics, 181( 3), 1085-1097. doi:10.1007/s00431-021-04298-3
NLM
Matsushita FY, Krebs VLJ, Carvalho WB de. Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach [Internet]. European journal of pediatrics. 2022 ; 181( 3): 1085-1097.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1007/s00431-021-04298-3
Vancouver
Matsushita FY, Krebs VLJ, Carvalho WB de. Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach [Internet]. European journal of pediatrics. 2022 ; 181( 3): 1085-1097.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1007/s00431-021-04298-3

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