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(v2.1.1.9186) replace antibiotics with antimicrobials!

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dr. M.S. (Matthijs) Berends
2025-03-07 20:43:26 +01:00
parent f2b2a450cb
commit f7938289eb
140 changed files with 4870 additions and 4702 deletions
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4
vignettes/AMR_for_Python.Rmd
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@ -147,7 +147,7 @@ In this example, we generate an antibiogram by selecting various antibiotics.
## Taxonomic Data Sets Now in Python!
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
```python
AMR.microorganisms
@ -168,7 +168,7 @@ AMR.microorganisms
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
```python
AMR.antibiotics
AMR.antimicrobials
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |

2
vignettes/AMR_with_tidymodels.Rmd
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@ -63,7 +63,7 @@ data <- example_isolates %>%
**Explanation:**
- `aminoglycosides()` and `betalactams()` dynamically select columns for antibiotics in these classes.
- `aminoglycosides()` and `betalactams()` dynamically select columns for antimicrobials in these classes.
- `drop_na()` ensures the model receives complete cases for training.
### **Defining the Workflow**

2
vignettes/EUCAST.Rmd
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@ -51,7 +51,7 @@ oops
eucast_rules(oops, info = FALSE)
```
A more convenient function is `mo_is_intrinsic_resistant()` that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:
A more convenient function is `mo_is_intrinsic_resistant()` that uses the same guideline, but allows to check for one or more specific microorganisms or antimicrobials:
```{r, warning = FALSE, message = FALSE}
mo_is_intrinsic_resistant(

14
vignettes/datasets.Rmd
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@ -40,7 +40,7 @@ download_txt <- function(filename) {
msg <- paste0(
"It was last updated on ",
trimws(format(file.mtime(paste0("../data/", filename, ".rda")), "%e %B %Y %H:%M:%S %Z", tz = "UTC")),
". Find more info about the structure of this data set [here](https://msberends.github.io/AMR/reference/", ifelse(filename == "antivirals", "antibiotics", filename), ".html).\n"
". Find more info about the structure of this data set [here](https://msberends.github.io/AMR/reference/", ifelse(filename == "antivirals", "antimicrobials", filename), ".html).\n"
)
github_base <- "https://github.com/msberends/AMR/raw/main/data-raw/"
filename <- paste0("../data-raw/", filename)
@ -111,7 +111,7 @@ print_df <- function(x, rows = 6) {
}
```
All reference data (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) in this `AMR` package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata. We also provide tab-separated text files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
All reference data (about microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) in this `AMR` package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata. We also provide tab-separated text files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
On this page, we explain how to download them and how the structure of the data sets look like.
@ -158,13 +158,13 @@ microorganisms %>%
```
## `antibiotics`: Antibiotic (+Antifungal) Drugs
## `antimicrobials`: Antibiotic and Antifungal Drugs
`r structure_txt(antibiotics)`
`r structure_txt(antimicrobials)`
This data set is in R available as `antibiotics`, after you load the `AMR` package.
This data set is in R available as `antimicrobials`, after you load the `AMR` package.
`r download_txt("antibiotics")`
`r download_txt("antimicrobials")`
The tab-separated text, Microsoft Excel, SPSS, and Stata files all contain the ATC codes, common abbreviations, trade names and LOINC codes as comma separated values.
@ -180,7 +180,7 @@ This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET,
### Example content
```{r, echo = FALSE}
antibiotics %>%
antimicrobials %>%
filter(ab %in% colnames(example_isolates)) %>%
print_df()
```

4
vignettes/welcome_to_AMR.Rmd
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@ -30,7 +30,7 @@ The `AMR` package is a [free and open-source](https://msberends.github.io/AMR/#c
This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
After installing this package, R knows `r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species and all `r AMR:::format_included_data_number(rbind(AMR::antibiotics[, "atc", drop = FALSE], AMR::antivirals[, "atc", drop = FALSE]))` antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data.
After installing this package, R knows `r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species and all `r AMR:::format_included_data_number(rbind(AMR::antimicrobials[, "atc", drop = FALSE], AMR::antivirals[, "atc", drop = FALSE]))` antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data.
With the help of contributors from all corners of the world, the `AMR` package is available in English, Czech, Chinese, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
@ -55,7 +55,7 @@ This package can be used for:
* Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to SIR
* Principal component analysis for AMR
All reference data sets (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) in this `AMR` package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find [all download links on our website](https://msberends.github.io/AMR/articles/datasets.html), which is automatically updated with every code change.
All reference data sets (about microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) in this `AMR` package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find [all download links on our website](https://msberends.github.io/AMR/articles/datasets.html), which is automatically updated with every code change.
This R package was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl), and is being [actively and durably maintained](https://msberends.github.io/AMR/news/) by two public healthcare organisations in the Netherlands.