Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study-Reference-Cited by-同舟云学术

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

Published:2018-12-22 Issue:4 Volume:74 Page:1108-1115
ISSN:0305-7453
Container-title:Journal of Antimicrobial Chemotherapy
language:en
Short-container-title:

Author:

Rawson T M¹²^ORCID,Hernandez B³,Moore L S P¹²,Blandy O¹,Herrero P³,Gilchrist M²,Gordon A⁴,Toumazou C³,Sriskandan S¹²,Georgiou P³,Holmes A H¹²

Affiliation:

1. National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK

2. Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, UK

3. Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK

4. Section of Anaesthetics, Pain Medicine & Intensive Care, Imperial College London, South Kensington Campus, London, UK

Funder

National Institute for Health Research Invention for Innovation

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Pharmacology (medical),Pharmacology,Microbiology (medical)

Link

http://academic.oup.com/jac/article-pdf/74/4/1108/28078187/dky514.pdf

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4. Prognostic accuracy of the SOFA Score, SIRS Criteria, and qSOFA Score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit;Raith;JAMA,2017

5. Identify sepsis in patients by using early warning scores, doctors are urged;Kmietowicz;BMJ,2015

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