data(ehr_data) data <- PhecapData(ehr_data, "healthcare_utilization", "label", 0.4, patient_id = "patient_id") 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.
system.time(feature_selected <- phecap_run_feature_extraction(data, surrogates))
Train phenotyping model and show the fitted model, with the AUC on the training set as well as random splits
suppressWarnings(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 round(validation$valid_roc[validation$valid_roc[, "FPR"] <= 0.2, ], 3)
Apply the model to all the patients to obtain predicted phenotype.
phenotype <- phecap_predict_phenotype(data, model) idx <- which.min(abs(validation$valid_roc[, "FPR"] - 0.05)) cut.fpr95 <- validation$valid_roc[idx, "cutoff"] case_status <- ifelse(phenotype$prediction >= cut.fpr95, 1, 0) predict.table <- cbind(phenotype, case_status) predict.table[1:10, ]
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.