Source: PLOS One. Unidades: FSP, PRÓ-G
Subjects: COVID-19, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, ESTUDOS DE COORTES, MORTALIDADE, ALGORITMOS
ABNT
MAGALHÃES, Júlia Chaves Neuenschwander e CHIAVEGATTO FILHO, Alexandre Dias Porto. Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients. PLOS One, v. 21, n. 3, 2026Tradução . . Disponível em: https://repositorio.usp.br/directbitstream/a0495fcf-5b50-4c10-a82e-8c0135d132e8/HEP_03_2026.pdf. Acesso em: 18 abr. 2026.APA
Magalhães, J. C. N., & Chiavegatto Filho, A. D. P. (2026). Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients. PLOS One, 21( 3). doi:10.1371/journal.pone.0344354NLM
Magalhães JCN, Chiavegatto Filho ADP. Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients [Internet]. PLOS One. 2026 ;21( 3):[citado 2026 abr. 18 ] Available from: https://repositorio.usp.br/directbitstream/a0495fcf-5b50-4c10-a82e-8c0135d132e8/HEP_03_2026.pdfVancouver
Magalhães JCN, Chiavegatto Filho ADP. Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients [Internet]. PLOS One. 2026 ;21( 3):[citado 2026 abr. 18 ] Available from: https://repositorio.usp.br/directbitstream/a0495fcf-5b50-4c10-a82e-8c0135d132e8/HEP_03_2026.pdf
