Author: Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek
Date: 2026-03-09

# Adding Custom Patient-Level Prediction Algorithms

#### Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek

#### 2026-03-09

# Introduction

This vignette describes how you can add your own custom algorithms in the Observational Health Data Sciencs and Informatics (OHDSI) `PatientLevelPrediction` package. This allows you to fully leverage the OHDSI PatientLevelPrediction framework for model development and validation. 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 algorithms with the OHDSI community through our GitHub repository.

# Algorithm Code Structure

Each algorithm in the package should be implemented in its own <Name>.R file, e.g. KNN.R, containing a set<Name> function, a fit<Name> function and a predict<Name> function. Occasionally the fit and prediction functions may be reused (if using an R classifier see RClassifier.R or if using a scikit-learn classifier see SklearnClassifier.R). We will now describe each of these functions in more detail below.

## Set

The set<Name> is a function that takes as input the different hyper-parameter values to do a grid search when training. The output of the functions needs to be a list as class `modelSettings` containing:

- param - a list of hyper-parameter values that can be used by `createHyperparameterSettings`
- settings - the settings such as modelName, trainRFunction, predictRFunction, varImpRFunction and seed
- fitFunction - a string specifying what function to call to fit the model, generally the existing fitBinaryClassifier can be used for most models

The param object can have a setttings attribute containing any extra settings. For example to specify the model name and the seed used for reproducibility:

    settings <- list(
      seed = 12,
      modelName = "Special classifier",
      trainRFunction = 'madeupFit', # this will be called to train the made up model
      predictRFunction = 'madeupPrediction', # this will be called to make predictions
      varImpRFunction = 'madeupVarImp' # this will be called to get variable importance
    )

For example, if you were adding a model called madeUp that has two hyper-parameters then the set function should be:

    setMadeUp <- function(a = c(1, 4, 10), b = 2, seed = NULL) {
      # add input checks here...

      param <- list(
        a = a,
        b = b
        )

      settings <- list(
        modelName = "Made Up",
        requiresDenseMatrix = TRUE,
        seed = seed,
        trainRFunction = 'madeupFit', # this will be called to train the made up model
        predictRFunction = 'madeupPrediction', # this will be called to make predictions
        varImpRFunction = 'madeupVarImp', # this will be called to get variable importance
        saveToJson = TRUE,
        saveType = "file"
      )

      # now create list of all combinations:
      result <- list(
        fitFunction = "fitBinaryClassifier", # this will be called to train the made up model
        param = param,
        settings = settings
      )
      class(result) <- "modelSettings"

      return(result)
    }

## Fit

This function specified by `trainRFunction` should train your custom model for a given hyper parameter setting. The takes as input:

- dataMatrix - a sparse matrix with rows as patient and columns as features
- labels - a list where labels$outcomeCount is the class label per patient
- hyperParameters - a named list of the hyperparameters
- settings - the settings object defined in the set function that has details such as seed.

The output should be the trained model.

    fitMadeUp <- function(
        dataMatrix,
        labels,
        hyperParameters,
        settings
        ) {
      # set the seed for reproducibility
      set.seed(settings$seed)

      # add code here to call a function to train the classifier using the data
      # for the specified hyperparameters that are a named list in hyperParameters
      model <- madeUpModel(
        X = dataMatrix,
        Y = labels$outcomeCount,
        hyperparameter1 = hyperParameters$hyperparameter1,
        hyperparameter2 = hyperParameters$hyperparameter2
        )

      return(model)
    }

## Predict

The prediction function specified by `predictRFunction` takes as input:

- plpModel: the model that was fit using trainRFunction
- data: either a new plpData object or the output of toSparseM()
- cohort: a target cohort or population data.frame

The predict function needs to apply the model and then add a column called ‘value’ to the cohort data.frame with the predicted risks per patient.

## VarImp

The varImp function `varImpRFunction` must take as input the model and the covariateMap that is a data.frame with columns including covariateId, columnId.

The output is covariateMap data.frame with two additional columns:

- included: (0 or 1) specifying whether the variable was used in the model
- covariateValue: the variable importance for the variable

# Algorithm Example

Below a fully functional algorithm example is given, however we highly recommend you to have a look at the available algorithms in the package (see GradientBoostingMachine.R for the set function, RClassifier.R for the fit and prediction function for R classifiers).

## Set

    setMadeUp <- function(a = c(1, 4, 6), b = 2, seed = NULL) {
      # add input checks here...

      if (is.null(seed)) {
        seed <- sample(100000, 1)
      }

      param <- list(
          a = a,
          b = b
        )

      settings <- list(
        modelName = "Made Up",
        requiresDenseMatrix = TRUE,
        seed = seed,
        trainRFunction = 'fitMadeUp',
        predictRFunction = 'predictMadeUp',
        varImpRFunction = 'varImpMadeUp',
        saveToJson = TRUE,
        saveType = "file"
      )

      # now create list of all combinations:
      result <- list(
        fitFunction = "fitBinaryClassifier", # this is an existing wrapper fit function
        param = param,
        settings = settings
      )
      class(result) <- "modelSettings"

      return(result)
    }

## Fit

The fit function needs to take a dataMatrix with the features per patient, a label vector with the class labels for each patient, the hyperparameters and the model settings.

    fitMadeUp <- function(
        dataMatrix,
        labels,
        hyperParameters,
        settings
        ) {
      # set the seed for reproducibility
      set.seed(settings$seed)

      # add code here to call a function to train the classifier using the data
      # for the specified hyperparameters that are a named list in hyperParameters
      model <- madeUpModel(
        X = dataMatrix,
        Y = labels$outcomeCount,
        hyperparameter1 = hyperParameters$hyperparameter1,
        hyperparameter2 = hyperParameters$hyperparameter2
        )

      return(model)
    }

## Predict

The final step is to create a predict function for the model. In the example above the prediction function in the settings was `predictRFunction = 'predictMadeUp'`, so a `predictMadeUp` function is required when applying the model. The predict function needs to take as input the plpModel returned by the fit function, new data to apply the model on and the cohort specifying the patients of interest to make the prediction for.

    predictMadeUp <- function(plpModel, data, cohort) {
      if (class(data) == "plpData") {
        # convert
        matrixObjects <- toSparseM(
          plpData = data,
          cohort = cohort,
          map = plpModel$covariateImportance %>%
            dplyr::select("columnId", "covariateId")
        )

        newData <- matrixObjects$dataMatrix
        cohort <- matrixObjects$labels
      } else {
        newData <- data
      }

      if (class(plpModel) == "plpModel") {
        model <- plpModel$model
      } else {
        model <- plpModel
      }

      cohort$value <- stats::predict(model, newData)

      # fix the rowIds to be the old ones
      # now use the originalRowId and remove the matrix rowId
      cohort <- cohort %>%
        dplyr::select(-"rowId") %>%
        dplyr::rename(rowId = "originalRowId")

      attr(cohort, "metaData") <- list(modelType = attr(plpModel, "modelType"))
      return(cohort)
    }

As the madeup model uses the standard R prediction, it has the same prediction function as xgboost, so we could have not added a new prediction function and instead made the `predictRFunction = 'predictXgboost'`.

## Variable importance

Each classifier needs a variable importance function specified. This takes as input the model and a data.frame called covariateMap that specifies the covariateIds and their corresponding columnId in the feature matrix.

    varImpMadeUp <- function(
        model,
        covariateMap
        ) {

      variableImportance <- tryCatch(
          {
            varImp <- reticulate::py_to_r(model$feature_importances_)
            varImp[is.na(varImp)] <- 0

            covariateMap$covariateValue <- unlist(varImp)
            covariateMap$included <- 1
          },
          error = function(e) {
            ParallelLogger::logInfo(e)
            return(NULL)
          }
        )

      return(variableImportance)
    }

# 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.