library(PheCAP)
set.seed(123)
Generate simulated data.
latent <- rgamma(8000, 0.3) latent2 <- rgamma(8000, 0.3) ehr_data <- data.frame( patient_id = 1:8000, ICD1 = rpois(8000, 7 * (rgamma(8000, 0.2) + latent) / 0.5), ICD2 = rpois(8000, 6 * (rgamma(8000, 0.8) + latent) / 1.1), ICD3 = rpois(8000, 1 * rgamma(8000, 0.5 + latent2) / 0.5), ICD4 = rpois(8000, 2 * rgamma(8000, 0.5) / 0.5), NLP1 = rpois(8000, 8 * (rgamma(8000, 0.2) + latent) / 0.6), NLP2 = rpois(8000, 2 * (rgamma(8000, 1.1) + latent) / 1.5), NLP3 = rpois(8000, 5 * (rgamma(8000, 0.1) + latent) / 0.5), NLP4 = rpois(8000, 11 * rgamma(8000, 1.9 + latent) / 1.9), NLP5 = rpois(8000, 3 * rgamma(8000, 0.5 + latent2) / 0.5), NLP6 = rpois(8000, 2 * rgamma(8000, 0.5) / 0.5), NLP7 = rpois(8000, 1 * rgamma(8000, 0.5) / 0.5), HU = rpois(8000, 30 * rgamma(8000, 0.1) / 0.1), label = NA) ii <- sample.int(8000, 400) ehr_data[ii, "label"] <- with( ehr_data[ii, ], rbinom(400, 1, plogis( -5 + 1.5 * log1p(ICD1) + log1p(NLP1) + 0.8 * log1p(NLP3) - 0.5 * log1p(HU)))) head(ehr_data) tail(ehr_data)
Define features and labels used for phenotyping.
data <- PhecapData(ehr_data, "HU", "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. The default lower_cutoff is 1, and the default upper_cutoff is 10. In some cases one may want to define surrogate through lab test. Feel free to change the cutoffs based on domain knowledge.
surrogates <- list( PhecapSurrogate( variable_names = "ICD1", lower_cutoff = 1, upper_cutoff = 10), PhecapSurrogate( variable_names = "NLP1", 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)) feature_selected
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)
phecap_plot_roc_curves(validation)
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, ]
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