Introduction

This vignette describes how you can add your own custom function for feature engineering in the Observational Health Data Sciences 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 vignette('BuildingPredictiveModels').

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$covariateDatarestricted 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.

createAgeSpline <- function(knots = 5) {
  # create list of inputs to implement function
  featureEngineeringSettings <- list(
    knots = knots
  )

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

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

implementAgeSplines <- function(trainData, featureEngineeringSettings, model = NULL) {
  # if there is a model, it means this function is called through applyFeatureengineering, meaning it   # should apply the model fitten on training data to the test data
  if (is.null(model)) {
    knots <- featureEngineeringSettings$knots
    ageData <- trainData$labels
    y <- ageData$outcomeCount
    X <- ageData$ageYear
    model <- mgcv::gam(
      y ~ s(X, bs = "cr", k = knots, m = 2)
    )
    newData <- data.frame(
      rowId = ageData$rowId,
      covariateId = 2002,
      covariateValue = model$fitted.values
    )
  } else {
    ageData <- trainData$labels
    X <- trainData$labels$ageYear
    y <- ageData$outcomeCount
    newData <- data.frame(y = y, X = X)
    yHat <- predict(model, newData)
    newData <- data.frame(
      rowId = trainData$labels$rowId,
      covariateId = 2002,
      covariateValue = yHat
    )
  }

  # remove existing age if in covariates
  trainData$covariateData$covariates <- trainData$covariateData$covariates |>
    dplyr::filter(!.data$covariateId %in% c(1002))

  # update covRef
  Andromeda::appendToTable(
    trainData$covariateData$covariateRef,
    data.frame(
      covariateId = 2002,
      covariateName = "Cubic restricted age splines",
      analysisId = 2,
      conceptId = 2002
    )
  )

  # update covariates
  Andromeda::appendToTable(trainData$covariateData$covariates, newData)

  featureEngineering <- list(
    funct = "implementAgeSplines",
    settings = list(
      featureEngineeringSettings = featureEngineeringSettings,
      model = model
    )
  )

  attr(trainData$covariateData, "metaData")$featureEngineering <- listAppend(
    attr(trainData$covariateData, "metaData")$featureEngineering,
    featureEngineering
  )

  return(trainData)
}

Acknowledgments

Considerable work has been dedicated to provide the PatientLevelPrediction package.

citation("PatientLevelPrediction")

## To cite PatientLevelPrediction in publications use:
## 
##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
##   and implementation of a standardized framework to generate and
##   evaluate patient-level prediction models using observational
##   healthcare data." _Journal of the American Medical Informatics
##   Association_, *25*(8), 969-975.
##   <https://doi.org/10.1093/jamia/ocy032>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     author = {J. M. Reps and M. J. Schuemie and M. A. Suchard and P. B. Ryan and P. Rijnbeek},
##     title = {Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data},
##     journal = {Journal of the American Medical Informatics Association},
##     volume = {25},
##     number = {8},
##     pages = {969-975},
##     year = {2018},
##     url = {https://doi.org/10.1093/jamia/ocy032},
##   }

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.