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

Critical Care Explorations

Volumn 1, Issue 10, 2019, Pages E0053-

  Henry, Katharine E ^a   Hager, David N ^b   Osborn, Tiffany M ^c   Wu, Albert W ^d   Saria, Suchi ^a,d  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

electronic data processing; electronic health records; informatics; machine learning; sepsis; severe sepsis

Indexed keywords

EID: 85083835198     PISSN: None     EISSN: 26398028     Source Type: Journal    
DOI: 10.1097/CCE.0000000000000053     Document Type: Article

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