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An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes.
Edmondson, Mackenzie J; Luo, Chongliang; Duan, Rui; Maltenfort, Mitchell; Chen, Zhaoyi; Locke, Kenneth; Shults, Justine; Bian, Jiang; Ryan, Patrick B; Forrest, Christopher B; Chen, Yong.
Afiliação
  • Edmondson MJ; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Luo C; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Duan R; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Maltenfort M; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Chen Z; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Locke K; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA.
  • Shults J; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Bian J; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Ryan PB; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Forrest CB; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA.
  • Chen Y; Janssen Research and Development, Titusville, NJ, USA.
Sci Rep ; 11(1): 19647, 2021 10 04.
Article em En | MEDLINE | ID: mdl-34608222
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Modelos Estatísticos / Atenção à Saúde Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Modelos Estatísticos / Atenção à Saúde Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos