Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy-Reference-Cited by-同舟云学术

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Published:2020-01-21 Issue:3 Volume:46 Page:383-400
ISSN:0342-4642
Container-title:Intensive Care Medicine
language:en
Short-container-title:Intensive Care Med

Author:

Fleuren Lucas M.^ORCID,Klausch Thomas L. T.,Zwager Charlotte L.,Schoonmade Linda J.,Guo Tingjie,Roggeveen Luca F.,Swart Eleonora L.,Girbes Armand R. J.,Thoral Patrick,Ercole Ari,Hoogendoorn Mark,Elbers Paul W. G.

Publisher

Springer Science and Business Media LLC

Subject

Critical Care and Intensive Care Medicine

Link

http://link.springer.com/content/pdf/10.1007/s00134-019-05872-y.pdf

Reference71 articles.

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2. Rhee C, Dantes R, Epstein L et al (2017) Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009–2014. JAMA 318:1241. https://doi.org/10.1001/jama.2017.13836

3. Álvaro-Meca A, Jiménez-Sousa MA, Micheloud D et al (2018) Epidemiological trends of sepsis in the twenty-first century (2000–2013): an analysis of incidence, mortality, and associated costs in Spain. Popul Health Metr 16:4. https://doi.org/10.1186/s12963-018-0160-x

4. Seymour CW, Gesten F, Prescott HC et al (2017) Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med 376:2235–2244. https://doi.org/10.1056/NEJMoa1703058

5. Liu VX, Fielding-Singh V, Greene JD et al (2017) The timing of early antibiotics and hospital mortality in sepsis. Am J Respir Crit Care Med 196:856–863. https://doi.org/10.1164/rccm.201609-1848OC

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