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Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.
Edmondson, Mackenzie J; Luo, Chongliang; Nazmul Islam, Md; Sheils, Natalie E; Buresh, John; Chen, Zhaoyi; Bian, Jiang; Chen, Yong.
Affiliation
  • 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.
  • Nazmul Islam M; Optum Labs at UnitedHealth Group, Minnetonka, MN, USA.
  • Sheils NE; Optum Labs at UnitedHealth Group, Minnetonka, MN, USA.
  • Buresh J; Optum Labs at UnitedHealth Group, Minnetonka, MN, USA.
  • Chen Z; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA.
  • Bian J; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA.
  • Chen Y; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. Electronic address: Ychen123@upenn.edu.
J Biomed Inform ; 131: 104097, 2022 07.
Article de En | MEDLINE | ID: mdl-35643272
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: COVID-19 Type d'étude: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limites: Humans Langue: En Journal: J Biomed Inform Sujet du journal: INFORMATICA MEDICA Année: 2022 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: États-Unis d'Amérique
Texte intégral
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Imprimer
XML
PubMed Links
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: COVID-19 Type d'étude: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limites: Humans Langue: En Journal: J Biomed Inform Sujet du journal: INFORMATICA MEDICA Année: 2022 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: États-Unis d'Amérique