Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach-Reference-Cited by-同舟云学术

Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

Published:2019-11-12 Issue:3 Volume:128 Page:688-696
ISSN:1364-5072
Container-title:Journal of Applied Microbiology
language:en
Short-container-title:J Appl Microbiol

Author:

Chowdhury A.S.¹^ORCID,Lofgren E.T.²³^ORCID,Moehring R.W.⁴,Broschat S.L.¹²⁵

Affiliation:

1. School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

2. Paul G. Allen School for Global Animal Health Washington State University Pullman WA USA

3. Department of Mathematics and Statistics Washington State University Pullman WA USA

4. Department of Medicine Duke University School of Medicine Durham NC USA

5. Department of Veterinary Microbiology and Pathology Washington State University Pullman WA USA

Funder

Centers for Disease Control and Prevention

Publisher

Wiley

Subject

Applied Microbiology and Biotechnology,General Medicine,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jam.14499

Reference36 articles.

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3. Centers for Disease Control and Prevention (US)(2013)Antibiotic resistance threats in the United States 2013. US Department of Health and Human Services: Centers for Disease Control and Prevention.

4. Centers for Disease Control and Prevention(2016)National Healthcare Safety Network: antimicrobial use and resistance (AUR) options. Accessed March 31.

5. Chowdhury A.S. Alam M.M.andZhang Y.(2015)A biomarker ensemble ranking framework for prioritizing depression candidate genes. In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp.1–6) IEEE.

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