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Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.
Lindberg, David S; Prosperi, Mattia; Bjarnadottir, Ragnhildur I; Thomas, Jaime; Crane, Marsha; Chen, Zhaoyi; Shear, Kristen; Solberg, Laurence M; Snigurska, Urszula Alina; Wu, Yonghui; Xia, Yunpeng; Lucero, Robert J.
Afiliação
  • Lindberg DS; Department of Statistics, College of Liberal Arts and Sciences, University of Florida, United States. Electronic address: dlindberg@ufl.edu.
  • Prosperi M; Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, United States.
  • Bjarnadottir RI; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.
  • Thomas J; UF Health-Shands Hospital, United States.
  • Crane M; UF Health-Shands Hospital, United States.
  • Chen Z; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, United States.
  • Shear K; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.
  • Solberg LM; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States; NF/SG VAHS, Geriatrics Research, Education, and Clinical Center (GRECC) Gainesville, Florida, United States.
  • Snigurska UA; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.
  • Wu Y; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, United States.
  • Xia Y; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.
  • Lucero RJ; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.
Int J Med Inform ; 143: 104272, 2020 11.
Article em En | MEDLINE | ID: mdl-32980667
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registros Eletrônicos de Saúde / Pacientes Internados Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de publicação: Irlanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registros Eletrônicos de Saúde / Pacientes Internados Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de publicação: Irlanda