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< title > Welcome to the AMR package • AMR (for R)< / title >
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Conduct AMR analysis
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Predict antimicrobial resistance
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Data sets for download / own use
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Conduct principal component analysis for AMR
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Determine multi-drug resistance (MDR)
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Work with WHONET data
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Import data from SPSS/SAS/Stata
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Apply EUCAST rules
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Get properties of a microorganism
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Get properties of an antibiotic
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Other: benchmarks
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Changelog
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< h1 data-toc-skip > Welcome to the AMR package< / h1 >
< small class = "dont-index" > Source: < a href = "https://github.com/msberends/AMR/blob/master/vignettes/welcome_to_AMR.Rmd" > < code > vignettes/welcome_to_AMR.Rmd< / code > < / a > < / small >
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< p > < strong > READ ALL VIGNETTES < a href = "https://msberends.github.io/AMR/articles/" > ON OUR WEBSITE< / a > .< / strong > < / p >
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< a href = "#welcome-to-the-amr-package" class = "anchor" > < / a > Welcome to the AMR package< / h1 >
< p > < code > AMR< / code > is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. < strong > Our aim is to provide a standard< / strong > for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.< / p >
< p > After installing this package, R knows < strong > ~70,000 distinct microbial species< / strong > and all < strong > ~550 antibiotic, antimycotic and antiviral drugs< / strong > by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.< / p >
< p > This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). < strong > It was designed to work in any setting, including those with very limited resources< / strong > . It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained (see Changelog) and is free software (see Copyright).< / p >
< p > Since its first public release in early 2018, this package has been downloaded from more than 100 countries.< / p >
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< a href = "#usage-examples" class = "anchor" > < / a > Usage examples< / h2 >
< p > This package can be used for:< / p >
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< li > Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature< / li >
< li > Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines< / li >
< li > Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records< / li >
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< li > Determining first isolates to be used for AMR data analysis< / li >
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< li > Calculating antimicrobial resistance< / li >
< li > Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)< / li >
< li > Calculating (empirical) susceptibility of both mono therapy and combination therapies< / li >
< li > Predicting future antimicrobial resistance using regression models< / li >
< li > Getting properties for any microorganism (like Gram stain, species, genus or family)< / li >
< li > Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)< / li >
< li > Plotting antimicrobial resistance< / li >
< li > Applying EUCAST expert rules< / li >
< li > Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code< / li >
< li > Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code< / li >
< li > Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI< / li >
< li > Principal component analysis for AMR< / li >
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< p > Developed by < a href = "https://www.rug.nl/staff/m.s.berends/" > Matthijs S. Berends< / a > , < a href = "https://www.rug.nl/staff/c.f.luz/" > Christian F. Luz< / a > , < a href = "https://www.rug.nl/staff/a.w.friedrich/" > Alexander W. Friedrich< / a > , < a href = "https://www.rug.nl/staff/b.sinha/" > Bhanu N. M. Sinha< / a > , < a href = "https://www.rug.nl/staff/c.j.albers/" > Casper J. Albers< / a > , < a href = "https://www.rug.nl/staff/c.glasner/" > Corinna Glasner< / a > .< / p >
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