inst/shiny/PLPViewer/www/libraryDescription.md
In quinterpriest/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model
OHDSI PatientLevelPrediction Model Repository
This shiny application is an interface for viewing, uploading and downloading healthcare prediction models.
Our aim is to improve the reproducibility and transparency of prediction models. Reproducibiliy is improved by the ability to access code that can be run against any OMOP common data model data to implement and evaluate any model in this repository. Transparency is improved by providing all the details for the model development and performance within the app. This will help us decide which models to trust by examining extensive external validation results.
How to use?
Click on the 'Library' option in the left-hand menu to explore the models in the repository. You can select a model to explore further by selecting the corresponding row in the library table. This will then populate the settings/performance/model views.
How to download a model?
Click on the 'download development' to download an R package that can be executed on any OMOP common data model data to replicate the model development process in your data. Click the 'download validation' to download an R package that can be executed on any OMOP common data model data to validate the choosen model in your data. You can then upload the new model or validation results to the repo.
How to upload results?
Click the 'upload' left-hand menu option, fill out the form and click submit. The results will be reviewed and you will recieve and email once the results are in the repository.
Questions?
Please send any questions to: ...@...com
quinterpriest/PatientLevelPrediction documentation built on April 20, 2022, 12:50 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
OHDSI PatientLevelPrediction Model Repository
This shiny application is an interface for viewing, uploading and downloading healthcare prediction models.
Our aim is to improve the reproducibility and transparency of prediction models. Reproducibiliy is improved by the ability to access code that can be run against any OMOP common data model data to implement and evaluate any model in this repository. Transparency is improved by providing all the details for the model development and performance within the app. This will help us decide which models to trust by examining extensive external validation results.
How to use?
Click on the 'Library' option in the left-hand menu to explore the models in the repository. You can select a model to explore further by selecting the corresponding row in the library table. This will then populate the settings/performance/model views.
How to download a model?
Click on the 'download development' to download an R package that can be executed on any OMOP common data model data to replicate the model development process in your data. Click the 'download validation' to download an R package that can be executed on any OMOP common data model data to validate the choosen model in your data. You can then upload the new model or validation results to the repo.
How to upload results?
Click the 'upload' left-hand menu option, fill out the form and click submit. The results will be reviewed and you will recieve and email once the results are in the repository.
Questions?
Please send any questions to: ...@...com
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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