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Paper Details

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials.
Crit Care
17
2022
Author NameAffiliation
John A KellumChildren's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh
Robert A BergChildren's Hospital of Philadelphia
David L WesselChildren's National Hospital
Murray M PollackChildren's National Hospital
Kathleen L MeertChildren's Hospital of Michigan
Kathleen L MeertCentral Michigan University
Mark W HallThe Research Institute at Nationwide Children's Hospital Immune Surveillance Laboratory, and Nationwide Children's Hospital
Christopher J L NewthChildren's Hospital Los Angeles
John C LinSt. Louis Children's Hospital
Allan DoctorSt. Louis Children's Hospital
Rick HarrisonC. S. Mott Children's Hospital
Athena F ZuppaUniversity of Pittsburgh
Ron W ReederMattel Children's Hospital at University of California Los Angeles
Richard HolubkovMattel Children's Hospital at University of California Los Angeles
Jonathan M DeanMattel Children's Hospital at University of California Los Angeles
Joseph A CarcilloChildren's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh
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