Open AccessMachine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit
Author(s) -
Prabij Dhungana,
Laura Piccolo Serafim,
Arnaldo Lopez Ruiz,
Danette Bruns,
Timothy Weister,
Nathan J. Smischney,
Rahul Kashyap
Publication year - 2019
Publication title -
world journal of critical care medicine
Language(s) - English
Resource type - Journals
ISSN - 2220-3141
DOI - 10.5492/wjccm.v8.i7.120
Subject(s) - medicine , septic shock , sepsis , intensive care unit , sofa score , cohort , intensive care , retrospective cohort study , medical record , machine learning , intensive care medicine , emergency medicine , computer science
With the recent change in the definition (Sepsis-3 Definition) of sepsis and septic shock, an electronic search algorithm was required to identify the cases for data automation. This supervised machine learning method would help screen a large amount of electronic medical records (EMR) for efficient research purposes.