Machine learning for prediction of septic shock at initial triage in emergency department-Reference-Cited by-同舟云学术

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Machine learning for prediction of septic shock at initial triage in emergency department

Published:2020-02 Issue: Volume:55 Page:163-170
ISSN:0883-9441
Container-title:Journal of Critical Care
language:en
Short-container-title:Journal of Critical Care

Author:

Kim Joonghee,Chang HyungLan,Kim Doyun,Jang Dong-Hyun,Park Inwon,Kim Kyuseok

Funder

Seoul National University.

Seoul National University

Publisher

Elsevier BV

Subject

Critical Care and Intensive Care Medicine

Reference43 articles.

1. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations;Fleischmann;Am J Respir Crit Care Med,2016

2. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program*;Ferrer;Crit Care Med,2014

3. Sepsis visits and antibiotic utilization in U.S. emergency departments*;Filbin;Crit Care Med,2014

4. Increasing critical care admissions from U.S. Emergency Departments, 2001–2009*;Herring;Crit Care Med,2013

5. Developing a new definition and assessing new clinical criteria for septic shock: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3);Shankar-Hari;JAMA,2016

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1. A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives;International Journal of Medical Informatics;2024-11

2. Early prediction of Sepsis: A comparative assessment on patients’ covariates;Biomedical Signal Processing and Control;2024-09

3. The AI Future of Emergency Medicine;Annals of Emergency Medicine;2024-08

4. Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis;Technology and Health Care;2024-06-22

5. Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study;Frontiers in Medicine;2024-06-05

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