validateExternal

validateExternal - Validate model performance on new data

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>.

Egill Fridgeirsson

PatientLevelPrediction

Develop Clinical Prediction Models Using the Common Data Model

Jenna Reps

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

Observational Health Data Science and Informatics 

validateExternal function

<dl><dt>validationDesignList</dt>
<dd>A list of objects created with <code>createValidationDesign</code></dd>
<dt>databaseDetails</dt>
<dd>A list of objects of class
<code>databaseDetails</code> created using <code>createDatabaseDetails</code></dd>
<dt>logSettings</dt>
<dd>An object of <code>logSettings</code> created
using <code>createLogSettings</code></dd>
<dt>outputFolder</dt>
<dd>The directory to save the validation results to
(subfolders are created per database in validationDatabaseDetails)</dd></dl>

Arguments

validateExternal - Validate model performance on new data — validateExternal

<dl>

<dt>validationDesignList</dt>
<dd>A list of objects created with <code>createValidationDesign</code></dd>


<dt>databaseDetails</dt>
<dd>A list of objects of class
<code>databaseDetails</code> created using <code>createDatabaseDetails</code></dd>


<dt>logSettings</dt>
<dd>An object of <code>logSettings</code> created
using <code>createLogSettings</code></dd>


<dt>outputFolder</dt>
<dd>The directory to save the validation results to
(subfolders are created per database in validationDatabaseDetails)</dd>

</dl>

validateExternal: validateExternal - Validate model performance on new data

Description

Usage

Value

Arguments