createFeatureEngineeringSettings

Create the settings for defining any feature engineering that will be done

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 

createFeatureEngineeringSettings function

<dl><dt>type</dt>
<dd>(character) Choice of:<ul>
<li>'none' No feature engineering - this is the default</li>
</ul></dd></dl>

Arguments

Create the settings for defining any feature engineering that will be done — createFeatureEngineeringSettings

<dl>

<dt>type</dt>
<dd>(character) Choice of:<ul>
<li>'none' No feature engineering - this is the default</li>
</ul></dd>

</dl>

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createFeatureEngineeringSettings: Create the settings for defining any feature engineering that will be done

Description

Usage

Value

Arguments

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Examples