Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.

Journal: Pediatric research
PMID: 39147905

Abstract

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.

Authors

  • Emma Holmes
    Division of Newborn Medicine, Mount Sinai Hospital, New York, NY, USA. emma.holmes@mountsinai.org.
  • Justin Kauffman
    Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Courtney Juliano
    Division of Newborn Medicine, Mount Sinai Hospital, New York, NY, USA.
  • Jennifer Duchon
    Division of Newborn Medicine, Mount Sinai Hospital, New York, NY, USA.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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