# Main Steps

    library(PheCAP)

Load data into R. The last argument of `PhecapData`, 0.4, refers to the percentage of labels reserved as test set.

    data(ehr_data)
    data <- PhecapData(ehr_data, "healthcare_utilization", "label", 0.4)
    data

Specify the surrogate used for surrogate-assisted feature extraction (SAFE). The typical way is to specify a main ICD code, a main NLP CUI, as well as their combination. In some cases one may want to define surrogate through lab test. The default lower_cutoff is 1, and the default upper_cutoff is 10. Feel free to change the cutoffs based on domain knowledge.

    surrogates <- list(
      PhecapSurrogate(
        variable_names = "main_ICD",
        lower_cutoff = 1, upper_cutoff = 10),
      PhecapSurrogate(
        variable_names = "main_NLP",
        lower_cutoff = 1, upper_cutoff = 10),
      PhecapSurrogate(
        variable_names = c("main_ICD", "main_NLP"),
        lower_cutoff = 1, upper_cutoff = 10))

Run surrogate-assisted feature extraction (SAFE) and show result.

    feature_selected <- phecap_run_feature_extraction(data, surrogates)
    feature_selected

Train phenotyping model and show the fitted model, with the AUC on the training set as well as random splits.

    model <- phecap_train_phenotyping_model(data, surrogates, feature_selected)
    model

Validate phenotyping model using validation label, and show the AUC and ROC.

    validation <- phecap_validate_phenotyping_model(data, model)
    validation
    phecap_plot_roc_curves(validation)

Apply the model to all the patients to obtain predicted phenotype.

    phenotype <- phecap_predict_phenotype(data, model)