Welcome to healthcare.ai

This package will get you started with healthcare machine learning in R.

Find our code at our Github Repo Our Github repo

What can you do with it?

  • Create and compare models based on your data.
  • Save and deploy a model.
  • Perform risk-adjusted comparisons.
  • Do trend analysis following Nelson rules.
  • Improve sparse data via longitudinal imputation.

How is it specific to healthcare?

  • Longitudinal machine learning via mixed models
  • Longitudinal imputation
  • Risk-adjusted comparisons

For those starting out

  • If you haven't, install R version >= 3.2.3 and RStudio

Note: if you're setting up R on an ETL server, don't download RStudio--simply open up RGui

Install the latest release on Windows

Open RStudio and work in the console


How to install the latest version on macOS

Note: If using macOS with healthcare.ai, you'll have to use csv files. We're working on adding MySQL connections. We'd love to hear which other databases your connecting to, so we can provide native support!

  • Open the Mac Terminal
  • Install Xcode compilers via xcode-select –install
  • Accept the Xcode license via sudo xcodebuild -license
  • Install Homebrew (the macOS package manager) with
  • Install ODBC driver via brew update && brew install unixODBC
  • Open R Studio
  • In the console, install RODBC from source with install.packages('RODBC',type = "source")
  • In the console, install other R healthcare.ai prerequisites via
install.packages(c('caret','data.table','doParallel','e1071','grpreg','lme4','lubridate','pROC','R6','ranger','ROCR'),repos = "https://cran.cnr.berkeley.edu/")
  • Install healthcare.ai

How to install latest version on Ubuntu (Linux)

  • Follow steps 1 and 2 here to install R
  • Run sudo apt-get install libiodbc2-dev
  • Run sudo apt-get install r-cran-rodbc
  • After typing R run install.packages('healthcareai')

Install the bleeding edge version (for folks providing contributions)

  • Grab prerequisites via the console of RGui or (preferably) RStudio
install.packages(c('caret','data.table','devtools','doParallel','e1071','grpreg','lme4','lubridate','pROC','R6','ranger','ROCR','RODBC'),repos = "https://cran.cnr.berkeley.edu/")


Tips on getting started

Built-in examples

Load the package you just installed and read the built-in docs


Website examples

See our docs website

Jupyter notebook examples

If you like Jupyter notebooks, check out step 1 and step 2 in model building with healthcareai.

Join the community

Read the blog and join the slack channel at healthcare.ai

What's new?

The CRAN 0.1.12 release features

  • Check the availability of columns after patient admit (and avoid target leak) via featureAvailabilityProfiler!
  • One can now deploy predictions to flat files via getOutDf. See ?getOutDf for more.

For issues

  • Double check that the code follows the examples in the built-in docs
  • Make sure you've thoroughly read the descriptions found here

  • If you're still seeing an error, file an issue on Stack Overflow using the healthcare-ai tag. Please provide

  • Details on your environment (OS, database type, R vs Py)
  • Goals (ie, what are you trying to accomplish)
  • Crystal clear steps for reproducing the error

How to help

Check out our github repo.