PheCAP-package: High-Throughput Phenotyping with EHR using a Common Automated...

In PheCAP: High-Throughput Phenotyping with EHR using a Common Automated Pipeline

Description

Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and Liao et al. (2015) <doi:10.1136/bmj.h1885>.

Details

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PheCAP provides a straightforward interface for conducting phenotyping on eletronic health records. One can specify the data via PhecapData, define surrogate using PhecapSurrogate. Next, one may run surrogate-assisted feature extraction (SAFE) by calling phecap_run_feature_extraction, and then train and validate phenotyping models via phecap_train_phenotyping_model and phecap_validate_phenotyping_model. The predictive performance can be visualized using phecap_plot_roc_curves. Predicted phenotype is provided by phecap_predict_phenotype.

Author(s)

Yichi Zhang [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut, cre]

Maintainer: PARSE LTD <[e-mail]>

References

Yu, S., Chakrabortty, A., Liao, K. P., Cai, T., Ananthakrishnan, A. N., Gainer, V. S., ... & Cai, T. (2016). Surrogate-assisted feature extraction for high-throughput phenotyping. Journal of the American Medical Informatics Association, 24(e1), e143-e149.

Liao, K. P., Cai, T., Savova, G. K., Murphy, S. N., Karlson, E. W., Ananthakrishnan, A. N., ... & Churchill, S. (2015). Development of phenotype algorithms using electronic medical records and incorporating natural language processing. bmj, 350, h1885.

Examples

# Simulate an EHR dataset
size <- 2000
latent <- rgamma(size, 0.3)
latent2 <- rgamma(size, 0.3)
ehr_data <- data.frame(
  ICD1 = rpois(size, 7 * (rgamma(size, 0.2) + latent) / 0.5),
  ICD2 = rpois(size, 6 * (rgamma(size, 0.8) + latent) / 1.1),
  ICD3 = rpois(size, 1 * rgamma(size, 0.5 + latent2) / 0.5),
  ICD4 = rpois(size, 2 * rgamma(size, 0.5) / 0.5),
  NLP1 = rpois(size, 8 * (rgamma(size, 0.2) + latent) / 0.6),
  NLP2 = rpois(size, 2 * (rgamma(size, 1.1) + latent) / 1.5),
  NLP3 = rpois(size, 5 * (rgamma(size, 0.1) + latent) / 0.5),
  NLP4 = rpois(size, 11 * rgamma(size, 1.9 + latent) / 1.9),
  NLP5 = rpois(size, 3 * rgamma(size, 0.5 + latent2) / 0.5),
  NLP6 = rpois(size, 2 * rgamma(size, 0.5) / 0.5),
  NLP7 = rpois(size, 1 * rgamma(size, 0.5) / 0.5),
  HU = rpois(size, 30 * rgamma(size, 0.1) / 0.1),
  label = NA)
ii <- sample.int(size, 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))))

# Define features and labels used for phenotyping.
data <- PhecapData(ehr_data, "HU", "label", validation = 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.
# 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.
feature_selected <- phecap_run_feature_extraction(
  data, surrogates, num_subsamples = 50, subsample_size = 200)
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, num_splits = 100)
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)


# A more complicated example

# Load Data.
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)

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