Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions.

Journal: Critical care explorations

Published Date: Oct 30, 2019

UNLABELLED: To develop and evaluate a novel strategy that automates the retrospective identification of sepsis using electronic health record data.

Katharine E Henry

Department of Computer Science, Johns Hopkins University, Baltimore, MD.
David N Hager

Division of Pulmonary and Critical Care, Department of Medicine, Johns Hopkins University, Baltimore, MD.
Tiffany M Osborn

Division of Emergency Medicine, Department of Surgery, Washington University, St. Louis, MO.
Albert W Wu

Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Suchi Saria

Department of Computer Science, Johns Hopkins University, Baltimore, MD.

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