Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning-Reference-Cited by-同舟云学术

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

Published:2022-01 Issue:1 Volume:28 Page:164-174
ISSN:1078-8956
Container-title:Nature Medicine
language:en
Short-container-title:Nat Med

Author:

Weis Caroline^ORCID,Cuénod Aline,Rieck Bastian^ORCID,Dubuis Olivier,Graf Susanne,Lang Claudia,Oberle Michael,Brackmann Maximilian^ORCID,Søgaard Kirstine K.,Osthoff Michael,Borgwardt Karsten^ORCID,Egli Adrian^ORCID

Funder

D-BSSE-Uni-Basel Personalised Medicine grant

Doc.Mobility fellowship by the Swiss National Science Foundation

Alfried Krupp von Bohlen und Halbach-Stiftung

Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung and D-BSSE-Uni-Basel Personalised Medicine grant

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Link

https://www.nature.com/articles/s41591-021-01619-9.pdf

Reference60 articles.

1. World Health Organization. Global Action Plan on Antimicrobial Resistance (WHO, 2016).

2. Wise, R. et al. Antimicrobial resistance. Is a major threat to public health. BMJ 317, 609–610 (1998).

3. Cassini, A. et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect. Dis. 19, 56–66 (2019).

4. Kumar, A. et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34, 1589–1596 (2006).

5. Seymour, C. W. et al. Time to treatment and mortality during mandated emergency care for sepsis. N. Engl. J. Med. 376, 2235–2244 (2017).

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