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Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia

Abstract:
Background: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance reduces the efficacy of empiric therapy guidelines derived from population data. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collect patient data, it would be possible to obtain... Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.12688/wellcomeopenres.14847.1

Authors


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Role:
Author
ORCID:
0000-0001-8490-2930
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Sub department:
Tropical Medicine
Oxford college:
Lincoln College
Role:
Author
ORCID:
0000-0002-0237-1070
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Publisher:
F1000Research Publisher's website
Journal:
Wellcome Open Research Journal website
Volume:
3
Issue:
131
Publication date:
2018-10-10
DOI:
EISSN:
2398-502X
Source identifiers:
927681
Keywords:
Pubs id:
pubs:927681
UUID:
uuid:5a6cc37f-e055-491e-9fbe-54a85f57266c
Local pid:
pubs:927681
Deposit date:
2018-11-21

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