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healthyR.ai

The Machine Learning and AI Modeling Companion to healthyR

To view the full wiki, click here: Full healthyR.ai Wiki

healthyR.ai is a comprehensive R package designed to streamline machine learning and AI workflows for healthcare data analysis. It provides an intuitive, consistent verb-based framework that simplifies data exploration, transformation, and modeling—taking the guesswork out of healthcare analytics.

✨ Key Features

🤖 AutoML Functions

Automated machine learning with hyperparameter tuning for multiple algorithms: - Tree-based models: C5.0, Cubist, Random Forest (Ranger), XGBoost - Support Vector Machines: Polynomial and RBF kernels - Linear models: GLMnet (elastic net regression) - Instance-based: K-Nearest Neighbors (KNN) - MARS: Multivariate Adaptive Regression Splines (Earth)

📊 Clustering & Dimensionality Reduction

  • K-Means AutoML: Automatic optimal cluster detection with scree plots and elbow methods
  • UMAP: Uniform Manifold Approximation and Projection for visualization
  • PCA: Principal Component Analysis with loadings plots

🔧 Data Preprocessing & Transformation

  • Scaling functions: Zero-one normalization, z-score standardization
  • Statistical transformations: Fourier, hyperbolic, polynomial transformations
  • Robust methods: Winsorization (truncate and move)
  • Custom recipe steps: Seamless integration with tidymodels

📈 Visualization & Analysis

  • Control charts: Statistical process control for healthcare metrics
  • Distribution analysis: Density plots, histograms, Q-Q plots
  • Statistical measures: Skewness, kurtosis, range statistics
  • Color-blind friendly palettes: Accessible visualizations

📦 Installation

Install the released version from CRAN:

install.packages("healthyR.ai")

Or get the development version from GitHub:

# install.packages("devtools")
devtools::install_github("spsanderson/healthyR.ai")

🚀 Quick Start

Control Charts for Healthcare Monitoring 

library(healthyR.ai)
library(ggplot2)

# Create sample adverse events data
data_tbl <- tibble::tibble(
  day = sample(c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"), 100, TRUE),
  person = sample(c("Tom", "Jane", "Alex"), 100, TRUE),
  count = rbinom(100, 20, ifelse(day == "Friday", .5, .2)),
  date = Sys.Date() - sample.int(100)
)

# Generate control chart
my_chart <- hai_control_chart(data_tbl, count, date)

my_chart +
  ylab("Number of Adverse Events") +
  scale_x_date(name = "Week of...", date_breaks = "week") +
  theme(axis.text.x = element_text(angle = -90, vjust = 0.5, hjust = 1))

AutoML with K-Nearest Neighbors 

library(healthyR.ai)
library(recipes)
library(dplyr)

# Prepare your data
data_split <- rsample::initial_split(mtcars, prop = 0.8)
train_data <- rsample::training(data_split)
test_data <- rsample::testing(data_split)

# Create a recipe
rec_obj <- recipes::recipe(mpg ~ ., data = train_data)

# Run AutoML KNN
knn_results <- hai_auto_knn(
  .data = train_data,
  .rec_obj = rec_obj,
  .best_metric = "rmse",
  .model_type = "regression"
)

# Access the best model
best_model <- knn_results$model_info %>%
  dplyr::filter(model_spec == knn_results$best_model_spec) %>%
  dplyr::pull(model)

K-Means Clustering with AutoML 

library(healthyR.ai)
library(dplyr)

# Prepare data
data_tbl <- healthyR.data::healthyR_data %>%
  dplyr::select(length_of_stay, age) %>%
  dplyr::filter(length_of_stay < 20)

# Auto K-Means - finds optimal clusters
kmeans_obj <- hai_kmeans_automl(
  .data = data_tbl,
  .max_clusters = 15
)

# Visualize the scree plot
hai_kmeans_scree_plot(kmeans_obj)

# Get user-item tibble for analysis
hai_kmeans_user_item_tbl(kmeans_obj)

Data Preprocessing with Custom Steps 

library(healthyR.ai)
library(recipes)

# Create a recipe with custom preprocessing steps
rec <- recipe(mpg ~ ., data = mtcars) %>%
  step_hai_scale_zscore(all_numeric_predictors()) %>%
  step_hai_fourier(disp, period = 365, order = 1) %>%
  step_hai_hyperbolic(hp, wt)

# Prep and bake
prepped_rec <- prep(rec)
transformed_data <- bake(prepped_rec, new_data = NULL)

📚 Documentation

🎯 Use Cases

healthyR.ai is designed for healthcare data analysts and data scientists who need to:

  • Predict length of stay for hospital patients
  • Forecast readmission rates with multiple algorithms
  • Monitor quality metrics using control charts
  • Identify patient cohorts through clustering
  • Preprocess and transform healthcare data for modeling
  • Automate hyperparameter tuning across multiple model types
  • Visualize distributions and identify outliers

🤝 Contributing

We welcome contributions! If you’d like to contribute to healthyR.ai:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

🐛 Issues & Feature Requests

Found a bug or have a feature request? Please file an issue at: https://github.com/spsanderson/healthyR.ai/issues

If you find this package useful, please leave a star on GitHub! ⭐

📖 Citation

If you use healthyR.ai in your research, please cite it:

citation("healthyR.ai")

👨‍💻 Author

Steven P. Sanderson II, MPH - Website: https://www.spsanderson.com/ - GitHub: @spsanderson - ORCID: 0009-0006-7661-8247

📄 License

MIT © Steven P. Sanderson II, MPH

🙏 Acknowledgments

healthyR.ai builds upon the excellent work of the tidymodels ecosystem and integrates seamlessly with: - recipes for data preprocessing - parsnip for unified model interfaces - tune for hyperparameter optimization - workflows for streamlined modeling pipelines - yardstick for model evaluation

Special thanks to all contributors and the R community for their support!