Open AccessClinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock*
Author(s) -
Jennifer C Ginestra,
H.M. Giannini,
William D. Schweickert,
Laurie Meadows,
Michael J. Lynch,
Kimberly Pavan,
Corey Chivers,
Michael Draugelis,
Patrick J. Donnelly,
Barry D. Fuchs,
Craig A Umscheid
Publication year - 2019
Publication title -
critical care medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.002
H-Index - 271
eISSN - 1530-0293
pISSN - 0090-3493
DOI - 10.1097/ccm.0000000000003803
Subject(s) - medicine , helpfulness , early warning score , warning system , sepsis , emergency medicine , observational study , septic shock , perception , medical emergency , nursing , psychology , social psychology , neuroscience , engineering , aerospace engineering
To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).