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(v3.0.0.9003) eucast_rules fix, new tidymodels integration
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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 analysing AMR data, including convenient antimicrobial selector functions like `aminoglycosides()` and `betalactams()`.
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In this post, we will explore how to use the `tidymodels` framework to predict resistance patterns in the `example_isolates` dataset in two examples.
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In this post, we will explore how to use the `tidymodels` framework to predict resistance patterns in the `example_isolates` dataset in two examples.
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This post contains the following examples:
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1. Using Antimicrobial Selectors
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2. Predicting ESBL Presence Using Raw MICs
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3. Predicting AMR Over Time
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## Example 1: Using Antimicrobial Selectors
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---
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## Example 2: Predicting ESBL Presence Using Raw MICs
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## Example 2: Predicting AMR Over Time
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In this second example, we demonstrate how to use `<mic>` columns directly in `tidymodels` workflows using AMR-specific recipe steps. This includes a transformation to `log2` scale using `step_mic_log2()`, which prepares MIC values for use in classification models.
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In this second example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.
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This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL).
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### **Objective**
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Our goal is to:
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1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
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2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
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3. Train a classification model and evaluate its predictive performance.
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### **Data Preparation**
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We use the `esbl_isolates` dataset that comes with the AMR package.
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```{r}
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# Load required libraries
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library(AMR)
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library(tidymodels)
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# View the esbl_isolates data set
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esbl_isolates
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# Prepare a binary outcome and convert to ordered factor
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data <- esbl_isolates %>%
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mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
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```
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**Explanation:**
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- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
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- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
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### **Defining the Workflow**
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#### 1. Preprocessing with a Recipe
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We use our `step_mic_log2()` function to log2-transform MIC values, ensuring that MICs are numeric and properly scaled. All MIC predictors can easily and agnostically selected using the new `all_mic_predictors()`:
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```{r}
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# Split into training and testing sets
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set.seed(123)
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split <- initial_split(data)
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training_data <- training(split)
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testing_data <- testing(split)
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# Define the recipe
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mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
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remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
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step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors
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# prep()
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mic_recipe
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```
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**Explanation:**
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- `remove_role()`: Removes irrelevant variables like genus.
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- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
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- `prep()`: Finalises the recipe based on training data.
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#### 2. Specifying the Model
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We use a simple logistic regression to model ESBL presence, though recent models such as xgboost ([link to `parsnip` manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html)) could be much more precise.
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```{r}
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# Define the model
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model <- logistic_reg(mode = "classification") %>%
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set_engine("glm")
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model
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```
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**Explanation:**
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- `logistic_reg()`: Specifies a binary classification model.
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- `set_engine("glm")`: Uses the base R GLM engine.
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#### 3. Building the Workflow
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```{r}
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# Create workflow
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workflow_model <- workflow() %>%
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add_recipe(mic_recipe) %>%
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add_model(model)
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workflow_model
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```
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### **Training and Evaluating the Model**
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```{r}
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# Fit the model
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fitted <- fit(workflow_model, training_data)
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# Generate predictions
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predictions <- predict(fitted, testing_data) %>%
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bind_cols(testing_data)
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# Evaluate model performance
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our_metrics <- metric_set(accuracy, kap, ppv, npv)
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metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
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metrics
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```
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**Explanation:**
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- `fit()`: Trains the model on the processed training data.
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- `predict()`: Produces predictions for unseen test data.
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- `metric_set()`: Allows evaluating multiple classification metrics.
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It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics$.estimate[3], 3) * 100`% and a negative predictive value (NPV) of `r round(metrics$.estimate[4], 3) * 100` using a simplistic logistic regression model.
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### **Visualising Predictions**
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We can visualise predictions by comparing predicted and actual ESBL status.
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```{r}
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library(ggplot2)
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ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
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geom_bar(position = "stack") +
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labs(title = "Predicted vs Actual ESBL Status",
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x = "Actual ESBL",
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y = "Count") +
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theme_minimal()
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```
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### **Conclusion**
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In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
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This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
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---
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## Example 3: Predicting AMR Over Time
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In this third example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.
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### **Objective**
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