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Load Data.
data(ehr_data)
data <- PhecapData(ehr_data, "healthcare_utilization", "label", 0.4, patient_id = "patient_id")
data## PheCAP Data
## Feature: 10000 observations of 587 variables
## Label: 119 yes, 62 no, 9819 missing
## Size of training samples: 109
## Size of validation samples: 72Specify 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.
## user system elapsed
## 242.632 0.132 242.856## Feature(s) selected by surrogate-assisted feature extraction (SAFE)
## [1] "main_ICD" "main_NLP" "NLP56" "NLP93" "NLP160" "NLP161" "NLP306"
## [8] "NLP403" "NLP536"Train phenotyping model and show the fitted model, with the AUC on the training set as well as random splits
## Phenotyping model:
## $lasso_bic
## (Intercept) main_ICD main_NLP
## 0.8165615 2.3789729 2.7761557
## main_ICD&main_NLP healthcare_utilization NLP56
## -1.6390179 -1.3316767 0.0000000
## NLP93 NLP160 NLP161
## -1.6091941 0.0000000 0.0000000
## NLP306 NLP403 NLP536
## 0.0000000 0.0000000 0.9910775
##
## AUC on training data: 0.988
## Average AUC on random splits: 0.971Validate phenotyping model using validation label, and show the AUC and ROC
## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' values
## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' values## AUC on validation data: 0.867
## AUC on training data: 0.988
## Average AUC on random splits: 0.971## cutoff pos.rate FPR TPR PPV NPV F1
## [1,] 1.000 0.007 0.000 0.245 1.000 0.353 0.394
## [2,] 0.988 0.319 0.000 0.356 1.000 0.390 0.526
## [3,] 0.975 0.361 0.000 0.468 1.000 0.436 0.637
## [4,] 0.966 0.375 0.048 0.545 0.965 0.463 0.697
## [5,] 0.959 0.375 0.048 0.603 0.969 0.497 0.743
## [6,] 0.936 0.458 0.048 0.661 0.971 0.536 0.786
## [7,] 0.861 0.542 0.048 0.718 0.973 0.582 0.827
## [8,] 0.785 0.569 0.048 0.776 0.975 0.637 0.864
## [9,] 0.772 0.583 0.095 0.784 0.952 0.633 0.860
## [10,] 0.770 0.583 0.095 0.784 0.952 0.633 0.860
## [11,] 0.767 0.583 0.095 0.784 0.952 0.633 0.860
## [12,] 0.764 0.583 0.095 0.784 0.952 0.633 0.860
## [13,] 0.762 0.597 0.143 0.785 0.930 0.621 0.851
## [14,] 0.756 0.597 0.143 0.789 0.931 0.626 0.854
## [15,] 0.750 0.597 0.143 0.793 0.931 0.630 0.856
## [16,] 0.742 0.611 0.143 0.797 0.931 0.635 0.859
## [17,] 0.732 0.611 0.143 0.801 0.932 0.640 0.861
## [18,] 0.718 0.625 0.190 0.804 0.911 0.630 0.854
## [19,] 0.696 0.625 0.190 0.804 0.911 0.630 0.854
## [20,] 0.674 0.625 0.190 0.804 0.911 0.630 0.854
## [21,] 0.652 0.625 0.190 0.804 0.911 0.630 0.854
## [22,] 0.631 0.625 0.190 0.804 0.911 0.630 0.854## Warning: Use of `df$value_x` is discouraged. Use `value_x` instead.## Warning: Use of `df$value_y` is discouraged. Use `value_y` instead.
Apply the model to all the patients to obtain predicted phenotype.
phenotype <- phecap_predict_phenotype(data, model)
idx <- which.min(abs(validation$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, ]## patient_id prediction case_status
## 1 1 1.467910e-01 0
## 2 2 9.999938e-01 1
## 3 3 1.828879e-01 0
## 4 4 9.384499e-05 0
## 5 5 1.649510e-02 0
## 6 6 9.803657e-01 1
## 7 7 8.645029e-03 0
## 8 8 2.602369e-04 0
## 9 9 3.652225e-01 0
## 10 10 7.273298e-04 0These 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.