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)