vignettes/AMR_with_tidymodels.Rmd
+This page was entirely written by our AMR for R +Assistant, a ChatGPT manually-trained model able to answer any +question about the AMR package. +
This page was entirely written by our AMR for R +Assistant, a ChatGPT manually-trained model able to answer any +question about the AMR package.
Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective treatments. The AMR R package provides robust tools for @@ -94,16 +99,15 @@ framework to predict resistance patterns in the example_isolates dataset.
AMR
example_isolates
By leveraging the power of tidymodels and the AMR package, we’ll build a reproducible machine learning -workflow to predict resistance to two important antibiotic classes: -aminoglycosides and beta-lactams.
tidymodels
Our goal is to build a predictive model using the -tidymodels framework to determine resistance patterns based -on microbial data. We will:
aminoglycosides()
betalactams()
Explanation: - aminoglycosides() and -betalactams() dynamically select columns for antibiotics in -these classes. - drop_na() ensures the model receives -complete cases for training.
drop_na()
Explanation:
We create a recipe to preprocess the data for modelling. This -includes: - Encoding resistance results (S, I, -R) as binary (resistant or not resistant). - Converting -microbial organism names (mo) into numerical features using -one-hot encoding.
S
I
R
mo
We create a recipe to preprocess the data for modelling.
# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% @@ -204,11 +205,18 @@ one-hot encoding. #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams())
Explanation: - step_mutate() transforms -resistance results (R) into binary variables (TRUE/FALSE). -- step_dummy() converts categorical organism -(mo) names into one-hot encoded numerical features, making -them compatible with the model.
step_mutate()
step_dummy()
recipe(mo ~ ., data = data)
step_corr()
Notice how the recipe contains just the antibiotic selector functions +- no need to define the columns specifically.
Explanation: - logistic_reg() sets up a -logistic regression model. - set_engine("glm") specifies -the use of R’s built-in GLM engine.
logistic_reg()
set_engine("glm")
# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe - add_model(logistic_model) # Add the logistic regression model -resistance_workflow -#> ══ Workflow ════════════════════════════════════════════════════════════════════ -#> Preprocessor: Recipe -#> Model: logistic_reg() -#> -#> ── Preprocessor ──────────────────────────────────────────────────────────────── -#> 1 Recipe Step -#> -#> • step_corr() -#> -#> ── Model ─────────────────────────────────────────────────────────────────────── -#> Logistic Regression Model Specification (classification) -#> -#> Computational engine: glm
Explanation: - initial_split() splits -the data into training and testing sets. - fit() trains the -workflow on the training set.
initial_split()
fit()
Notice how in fit(), the antibiotic selector functions +are internally called again. For training, these functions are called +since they are stored in the recipe.
Next, we evaluate the model on the testing data.
# Make predictions on the testing set @@ -351,17 +330,23 @@ workflow on the training set. #> <chr> <chr> <dbl> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989
Explanation: - predict() generates -predictions on the testing set. - metrics() computes -evaluation metrics like accuracy and AUC.
predict()
metrics()
It appears we can predict the Gram based on AMR results with a 0.995 -accuracy. The ROC curve looks like:
predictions %>% roc_curve(mo, `.pred_Gram-negative`) %>% autoplot()
This workflow is extensible to other antibiotic classes and resistance patterns, empowering users to analyse AMR data systematically and reproducibly.
vignettes/datasets.Rmd
datasets.Rmd
(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using the instructions here.)
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the University of Prince Edward Island’s Atlantic Veterinary College, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
rsi
sir
as.sir()
breakpoint_type = "animal"
host
ab
uti
as.sir(..., ab = "column1", mo = "column2", uti = "column3")
vctrs
dplyr
bind_rows()
rowwise()
c_across()
mic
.xpt
This changelog only contains changes from AMR v3.0 (October 2024) and later.