Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA
Author:
Affiliation:
1. Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USA.
2. Center for Health Systems Innovation, Oklahoma State University, Stillwater, OK, USA.
Publisher
The Korean Society of Medical Informatics
Subject
Health Information Management,Health Informatics,Biomedical Engineering
Link
https://synapse.koreamed.org/pdf/10.4258/hir.2018.24.2.139
Reference27 articles.
1. Prognostic Accuracy of Sepsis-3 Criteria for In-Hospital Mortality Among Patients With Suspected Infection Presenting to the Emergency Department
2. Impact of the Surviving Sepsis Campaign protocols on hospital length of stay and mortality in septic shock patients: Results of a three-year follow-up quasi-experimental study*
3. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
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