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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.