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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>.
Getting started
README.md
Adding Custom Data Splitting
Adding Custom Feature Engineering Functions
Adding Custom Patient-Level Prediction Algorithms
Adding Custom Sampling Functions
Automatically Build Multiple Patient-Level Predictive Models
Benchmark Tasks
Best Practice Research
Building patient-level predictive models
Clinical Models
Constrained Predictors
Creating Learning Curves
Integration of GIS Data Into OHDSI Model Building
Making patient-level predictive network study packages
Patient-Level Prediction Installation Guide
Browse package contents
Package details |
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| 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@erasmusmc.nl> |
| License | Apache License 2.0 |
| Version | 6.4.0 |
| URL | https://ohdsi.github.io/PatientLevelPrediction/ https://github.com/OHDSI/PatientLevelPrediction |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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