Source: European journal of pediatrics. Unidade: FM
Subjects: RECÉM-NASCIDO DE BAIXO PESO, FENÓTIPOS, APRENDIZADO COMPUTACIONAL
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: 25 set. 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/s00431-021-04298-3NLM
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 set. 25 ] Available from: https://doi.org/10.1007/s00431-021-04298-3Vancouver
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 set. 25 ] Available from: https://doi.org/10.1007/s00431-021-04298-3