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Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.
Holmes, Emma; Kauffman, Justin; Juliano, Courtney; Duchon, Jennifer; Nadkarni, Girish N.
Afiliación
  • Holmes E; Division of Newborn Medicine, Mount Sinai Hospital, New York, NY, USA. emma.holmes@mountsinai.org.
  • Kauffman J; Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. emma.holmes@mountsinai.org.
  • Juliano C; Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Duchon J; Division of Data Driven and Digital Medicine, Samuel Bronfman Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nadkarni GN; Division of Newborn Medicine, Mount Sinai Hospital, New York, NY, USA.
Pediatr Res ; 2024 Aug 15.
Article en En | MEDLINE | ID: mdl-39147905
ABSTRACT
IMPACT Novel artificial intelligence methods can aide in identification of cases of conditions using only unstructured electronic health record data. This graph-based method compares comprehensive electronic health records among neonates using temporal data. This provides a scalable solution to distinguish culture negative sepsis from rule out sepsis using a data-driven method.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Pediatr Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Pediatr Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos