Main Steps


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

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

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(
    variable_names = "main_ICD",
    lower_cutoff = 1, upper_cutoff = 10),
    variable_names = "main_NLP",
    lower_cutoff = 1, upper_cutoff = 10),
    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)

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)

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

validation <- phecap_validate_phenotyping_model(data, model)

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

phenotype <- phecap_predict_phenotype(data, model)

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PheCAP documentation built on Sept. 17, 2020, 5:07 p.m.