In quinterpriest/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

library(PatientLevelPrediction)

Introduction

This vignette describes how you can add your own custom function for feature engineering in the Observational Health Data Sciencs and Informatics (OHDSI) PatientLevelPrediction package. This vignette assumes you have read and are comfortable with building single patient level prediction models as described in the BuildingPredictiveModels vignette.

We invite you to share your new feature engineering functions with the OHDSI community through our GitHub repository.

Feature Engineering Function Code Structure

To make a custom feature engineering function that can be used within PatientLevelPrediction you need to write two different functions. The 'create' function and the 'implement' function.

The 'create' function, e.g., create\<FeatureEngineeringFunctionName>, takes the parameters of the feature engineering 'implement' function as input, checks these are valid and outputs these as a list of class 'featureEngineeringSettings' with the 'fun' attribute specifying the 'implement' function to call.

The 'implement' function, e.g., implement\<FeatureEngineeringFunctionName>, must take as input: * trainData - a list containing: - covariateData: the plpData$covariateData restricted to the training patients - labels: a data frame that contain rowId (patient identifier) and outcomeCount (the class labels) - folds: a data.frame that contains rowId (patient identifier) and index (the cross validation fold) * featureEngineeringSettings - the output of your create\<FeatureEngineeringFunctionName>

The 'implement' function can then do any manipulation of the trainData (adding new features or removing features) but must output a trainData object containing the new covariateData, labels and folds for the training data patients.

Example

Let's consider the situation where we wish to create an age spline feature. To make this custom feature engineering function we need to write the 'create' and 'implement' R functions.

Create function

Our age spline feature function will create a new feature using the plpData$cohorts ageYear column. We will implement a restricted cubic spline that requires specifying the number of knots. . Therefore, the inputs for this are: * knots an integer/double specifying the number of knots

createAgeSpine <- function(
                     knots = 5
                     ){

  # add input checks
  checkIsClass(knots, c('numeric','integer'))
  checkHigher(knots,0)

  # create list of inputs to implement function
  featureEngineeringSettings <- list(
    knots = knots
    )

  # specify the function that will implement the sampling
  attr(featureEngineeringSettings, "fun") <- "implementAgeSpine"

  # make sure the object returned is of class "sampleSettings"
  class(featureEngineeringSettings) <- "featureEngineeringSettings"
  return(featureEngineeringSettings)

}

We now need to create the 'implement' function implementAgeSpine()

Implement function

All 'implement' functions must take as input the trainData and the featureEngineeringSettings (this is the output of the 'create' function). They must return a trainData object containing the new covariateData, labels and folds.

In our example, the createAgeSpine() will return a list with 'knots'. The featureEngineeringSettings therefore contains this.

implementAgeSpine <- function(trainData, featureEngineeringSettings){

  # currently not used
  knots <- featureEngineeringSettings$knots


  # age in in trainData$labels as ageYear
  ageData <- trainData$labels

  # now implement the code to do your desired feature engineering

  data <- Matrix::sparseMatrix(
    i = 1:length(ageData$rowId),
    j = rep(1, length(ageData$rowId)),
    x = ageData$ageYear,
    dims=c(length(ageData$rowId),1)
  )

  data <- as.matrix(data)
  x <- data[,1]
  y <- ageData$outcomeCount

mRCS <- rms::ols(
  y~rms::rcs(x, 
             stats::quantile(
               x, 
               c(0, .05, .275, .5, .775, .95, 1),
               include.lowest = TRUE
               )
             )
  )

newData <- data.frame(
  rowId = ageData$rowId,
  covariateId = 2002,
  covariateValue = mRCS$fitted.values
  )

# add new data
Andromeda::appendToTable(tbl = trainData$covariateData$covariates, 
                         data = newData)

  # return the updated trainData
  return(trainData)
}

Acknowledgments

Considerable work has been dedicated to provide the PatientLevelPrediction package.

citation("PatientLevelPrediction")

Please reference this paper if you use the PLP Package in your work:

Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.

This work is supported in part through the National Science Foundation grant IIS 1251151.

quinterpriest/PatientLevelPrediction documentation built on April 20, 2022, 12:50 a.m.