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: 6.5.1
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
Published: 2025年10月15日
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>
NeedsCompilation: no
Materials: README, NEWS

Documentation:

Downloads:

macOS binaries: r-release (arm64): PatientLevelPrediction_6.5.1.tgz, r-oldrel (arm64): PatientLevelPrediction_6.5.1.tgz, r-release (x86_64): PatientLevelPrediction_6.5.1.tgz, r-oldrel (x86_64): PatientLevelPrediction_6.5.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=PatientLevelPrediction to link to this page.

AltStyle によって変換されたページ (->オリジナル) /