Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

Critical Care Medicine

Volumn 48, Issue 2, 2020, Pages 137-141

  Saria, Suchi ^a,b,c   Henry, Katharine E ^a  

^a Johns Hopkins University   (United States)

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

^c JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

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

Indexed keywords

ELECTRONIC HEALTH RECORD; HEALTH STATUS INDICATOR; HUMAN; MACHINE LEARNING; PATHOPHYSIOLOGY; REPRODUCIBILITY; SEPSIS;

ELECTRONIC HEALTH RECORDS; HEALTH STATUS INDICATORS; HUMANS; MACHINE LEARNING; REPRODUCIBILITY OF RESULTS; SEPSIS;

EID: 85077861705     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0000000000004144     Document Type: Editorial

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