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Using machine-learning methods to predict in-hospital mortality through the Elixhauser index: A Medicare data analysis.
Liu, Jianfang; Glied, Sherry; Yakusheva, Olga; Bevin, Cohen; Schlak, Amelia E; Yoon, Sunmoo; Kulage, Kristine M; Poghosyan, Lusine.
Afiliación
  • Liu J; Columbia University School of Nursing, New York City, New York, USA.
  • Glied S; Robert F. Wagner Graduate School of Public Service, New York University, New York City, New York, USA.
  • Yakusheva O; University of Michigan School of Nursing, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
  • Bevin C; Mount Sinai Health System, New York City, New York, USA.
  • Schlak AE; AAAS Science and Technology Policy Fellow, Office of Research and Development, U.S. Department of Veteran Affairs, Washington, DC, USA.
  • Yoon S; Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA.
  • Kulage KM; Office of Scholarship and Research Development, Columbia University School of Nursing, New York City, New York, USA.
  • Poghosyan L; Columbia University School of Nursing and Professor of Health Policy and Management, Mailman School of Public Health, Columbia University, Executive Director Center for Healthcare Delivery Research & Innovations (HDRI), New York City, New York, USA.
Res Nurs Health ; 46(4): 411-424, 2023 08.
Article en En | MEDLINE | ID: mdl-37221452
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medicare / Hospitalización Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans País/Región como asunto: America do norte Idioma: En Revista: Res Nurs Health Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medicare / Hospitalización Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans País/Región como asunto: America do norte Idioma: En Revista: Res Nurs Health Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos