Develop Clinical Prediction Models Using the Common Data Model [R package PatientLevelPrediction version 6.6.0]

PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Version:	6.6.0
Depends:	R (≥ 4.0.0)
Imports:	Andromeda, Cyclops (≥ 3.0.0), DatabaseConnector (≥ 6.0.0), digest, dplyr, FeatureExtraction (≥ 3.0.0), Matrix, memuse, ParallelLogger (≥ 2.0.0), pROC, PRROC, rlang, SqlRender (≥ 1.1.3), tidyr, utils
Suggests:	curl, Eunomia (≥ 2.0.0), glmnet, ggplot2, gridExtra, IterativeHardThresholding, knitr, lightgbm, Metrics, mgcv, OhdsiShinyAppBuilder (≥ 1.0.0), parallel, pkgload, polspline, readr, ResourceSelection, ResultModelManager (≥ 0.6.0), reticulate (≥ 1.41), rmarkdown, RSQLite, scoring, survival, survminer, testthat, withr, xgboost (> 1.3.2.1)
Published:	2026-03-09
DOI:	10.32614/CRAN.package.PatientLevelPrediction
Author:	Egill Fridgeirsson [aut, cre], Jenna Reps [aut], Martijn Schuemie [aut], Marc Suchard [aut], Patrick Ryan [aut], Peter Rijnbeek [aut], Observational Health Data Science and Informatics [cph]
Maintainer:	Egill Fridgeirsson <e.fridgeirsson at erasmusmc.nl>
BugReports:	https://github.com/OHDSI/PatientLevelPrediction/issues
License:	Apache License 2.0
URL:	https://ohdsi.github.io/PatientLevelPrediction/, https://github.com/OHDSI/PatientLevelPrediction
NeedsCompilation:	no
Citation:	PatientLevelPrediction citation info
Materials:	README, NEWS
CRAN checks:	PatientLevelPrediction results

Documentation:

Reference manual:

PatientLevelPrediction.html , PatientLevelPrediction.pdf

Vignettes:

Adding Custom Feature Engineering Functions (source, R code)
Adding Custom Patient-Level Prediction Algorithms (source, R code)
Adding Custom Sampling (source, R code)
Adding Custom Data Splitting (source, R code)
Benchmark Tasks (source)
Best Practices (source)
Automatically Build Multiple Patient-Level Predictive Models (source, R code)
Building patient-level predictive models (source, R code)
Clinical Models (source)
Constrained Predictors (source, R code)
Creating Learning Curves (source, R code)
Making patient-level predictive network study packages (source, R code)
Integration of GIS Data Into OHDSI Model Building (source, R code)
Patient-Level Prediction Installation Guide (source, R code)

Downloads:

Package source:	PatientLevelPrediction_6.6.0.tar.gz
Windows binaries:	r-devel: PatientLevelPrediction_6.6.0.zip, r-release: PatientLevelPrediction_6.6.0.zip, r-oldrel: PatientLevelPrediction_6.6.0.zip
macOS binaries:	r-release (arm64): PatientLevelPrediction_6.6.0.tgz, r-oldrel (arm64): PatientLevelPrediction_6.6.0.tgz, r-release (x86_64): PatientLevelPrediction_6.6.0.tgz, r-oldrel (x86_64): PatientLevelPrediction_6.6.0.tgz
Old sources:	PatientLevelPrediction archive

Linking:

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