# Adding Custom Feature Engineering Functions

#### Jenna Reps, Egill Fridgeirsson

#### 2025-02-10

# 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$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.

    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)
    }

We now need to create the ‘implement’ function `implementAgeSplines()`

## 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 `createAgeSpline()` will return a list with ‘knots’. The `featureEngineeringSettings` therefore contains this.

    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.