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Patient-Level Prediction Installation Guide

Jenna Reps, Peter R. Rijnbeek, Egill Fridgeirsson

2026-06-19

Source: vignettes/InstallationGuide.Rmd
InstallationGuide.Rmd

Introduction

This vignette describes how you need to install the Observational Health Data Science and Informatics (OHDSI) PatientLevelPrediction package under Windows, Mac, and Linux.

Software Prerequisites

Windows Users

Under Windows the OHDSI Patient Level Prediction (PLP) package requires installing:

Mac/Linux Users

Under Mac and Linux the OHDSI Patient Level Prediction (PLP) package requires installing:

Installing the Package

The preferred way to install the package is by using remotes, which will automatically install the latest release and all the latest dependencies.

If you do not want the official release you could install the bleeding edge version of the package (latest develop branch).

Note that the latest develop branch could contain bugs, please report them to us if you experience problems.

Installing PatientLevelPrediction using remotes

To install using remotes run:

install.packages("remotes")
remotes::install_github("OHDSI/PatientLevelPrediction")

When installing make sure to close any other Rstudio sessions that are using PatientLevelPrediction or any dependency. Keeping Rstudio sessions open can cause locks that prevent the package installing.

Python environment

Some classifiers in PatientLevelPrediction use a Python backend. In a standard online environment, reticulate will manage the Python requirements automatically when a Python model is used.

If you are in an offline or locked-down environment, configure Python outside PatientLevelPrediction and point reticulate to that Python binary before loading the package:

Sys.setenv(RETICULATE_PYTHON = "/path/to/python")
library(PatientLevelPrediction)

The Python environment must include the packages needed by the selected model, including scikit-learn.

Installation issues

Installation issues need to be posted in our issue tracker: https://github.com/OHDSI/PatientLevelPrediction/issues

The list below provides solutions for some common issues:

  1. If you have an error when trying to install a package in R saying ‘Dependancy X not available …’ then this can sometimes be fixed by running install.packages('X') and then once that completes trying to reinstall the package that had the error.

  2. I have found that using the github remotes to install packages can be impacted if you have multiple R sessions open as one session with a library open can cause the library to be locked and this can prevent an install of a package that depends on that library.

Common issues

Python environment Mac/Linux users

If reticulate is not selecting the intended Python environment, set the RETICULATE_PYTHON environment variable to the full path of the Python binary before loading PatientLevelPrediction.

Acknowledgments

Considerable work has been dedicated to provide the PatientLevelPrediction package.

citation("PatientLevelPrediction")
## To cite PatientLevelPrediction in publications use:
## 
##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
##   and implementation of a standardized framework to generate and
##   evaluate patient-level prediction models using observational
##   healthcare data." _Journal of the American Medical Informatics
##   Association_, *25*(8), 969-975.
##   <https://doi.org/10.1093/jamia/ocy032>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     author = {J. M. Reps and M. J. Schuemie and M. A. Suchard and P. B. Ryan and P. Rijnbeek},
##     title = {Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data},
##     journal = {Journal of the American Medical Informatics Association},
##     volume = {25},
##     number = {8},
##     pages = {969-975},
##     year = {2018},
##     url = {https://doi.org/10.1093/jamia/ocy032},
##   }

Please reference this paper if you use the PLP Package in your work:

Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.

This work is supported in part through the National Science Foundation grant IIS 1251151.