Your browser doesn't support javascript.
loading
Benchmarking emergency department prediction models with machine learning and public electronic health records.
Xie, Feng; Zhou, Jun; Lee, Jin Wee; Tan, Mingrui; Li, Siqi; Rajnthern, Logasan S/O; Chee, Marcel Lucas; Chakraborty, Bibhas; Wong, An-Kwok Ian; Dagan, Alon; Ong, Marcus Eng Hock; Gao, Fei; Liu, Nan.
Afiliação
  • Xie F; Centre for Quantitative Medicine and Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Zhou J; Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Lee JW; Centre for Quantitative Medicine and Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Tan M; Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Li S; Centre for Quantitative Medicine and Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Rajnthern LS; School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
  • Chee ML; Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia.
  • Chakraborty B; Centre for Quantitative Medicine and Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Wong AI; Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore.
  • Dagan A; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
  • Ong MEH; Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA.
  • Gao F; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Liu N; MIT Critical Data, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sci Data ; 9(1): 658, 2022 10 27.
Article em En | MEDLINE | ID: mdl-36302776
Assuntos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benchmarking / COVID-19 Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Singapura
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benchmarking / COVID-19 Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Singapura