Query millions of standardized medical concepts from R
Access SNOMED CT, ICD-10, RxNorm, LOINC, and 90+ OHDSI ATHENA vocabularies without downloading, installing, or maintaining local databases.
Documentation · API Reference · Examples
Working with OHDSI ATHENA vocabularies traditionally requires downloading multi-gigabyte files, setting up a database, and writing complex SQL queries. OMOPHub eliminates this friction.
| Traditional Approach | With OMOPHub |
|---|---|
| Download 5GB+ ATHENA vocabulary files | install.packages("omophub") |
| Set up and maintain database instance | One API call |
| Write complex SQL with multiple JOINs | Simple R functions |
| Manually update vocabularies quarterly | Always current data |
| Local infrastructure required | Works anywhere R runs |
# Install from CRAN
install.packages("omophub")
# Or install development version from GitHub
# install.packages("pak")
pak::pak("omopHub/omophub-R")library(omophub)
# Option 1: Environment variable (recommended)
Sys.setenv(OMOPHUB_API_KEY = "oh_xxxxxxxxx")
# Option 2: Use helper function
set_api_key("oh_xxxxxxxxx")
# Option 3: Store securely in system keyring
set_api_key("oh_xxxxxxxxx", store = "keyring")Get your API key from the OMOPHub Dashboard.
library(omophub)
# Create client
client <- OMOPHubClient$new()
# Get a concept by ID
concept <- client$concepts$get(201826)
concept$concept_name
# [1] "Type 2 diabetes mellitus"
# Search for concepts
results <- client$search$basic("metformin", vocabulary_ids = "RxNorm")
results$data
# Get concept by vocabulary code
snomed_concept <- client$concepts$get_by_code("SNOMED", "44054006")
# Map to another vocabulary
mappings <- client$mappings$get(201826, target_vocabularies = "ICD10CM")
# Navigate hierarchy
ancestors <- client$hierarchy$ancestors(201826, max_levels = 3)Validate and map clinical codes during OMOP CDM transformations:
# Validate source codes and find standard equivalents
validate_and_map <- function(source_vocab, source_code) {
concept <- client$concepts$get_by_code(source_vocab, source_code)
if (concept$standard_concept != "S") {
mappings <- client$mappings$get(
concept$concept_id,
target_vocabularies = "SNOMED"
)
return(mappings$mappings[[1]]$target_concept_id)
}
concept$concept_id
}
# Example: Map ICD-10 to SNOMED
standard_id <- validate_and_map("ICD10CM", "E11.9")Verify codes exist and are valid:
# Check if condition codes are valid
condition_codes <- c("E11.9", "I10", "J44.9")
for (code in condition_codes) {
tryCatch({
concept <- client$concepts$get_by_code("ICD10CM", code)
message(sprintf("OK %s: %s", code, concept$concept_name))
}, omophub_api_error = function(e) {
message(sprintf("ERROR %s: Invalid code!", code))
})
}Explore hierarchies to build comprehensive concept sets:
# Get all descendants for phenotype definition
descendants <- client$hierarchy$descendants(
201826, # Type 2 diabetes mellitus
max_levels = 5,
standard_only = TRUE
)
concept_set <- sapply(descendants$concepts, function(x) x$concept_id)
message(sprintf("Found %d concepts for T2DM phenotype", length(concept_set)))library(dplyr)
library(purrr)
# Search and convert to tibble
results <- client$search$basic("hypertension", page_size = 100)
concepts_df <- results$data %>%
map_dfr(~ tibble(
concept_id = .x$concept_id,
concept_name = .x$concept_name,
vocabulary_id = .x$vocabulary_id,
domain_id = .x$domain_id
))
# Filter and analyze
concepts_df %>%
filter(vocabulary_id == "SNOMED") %>%
count(domain_id)| Resource | Description | Key Methods |
|---|---|---|
concepts |
Concept lookup and batch operations | get(), get_by_code(), batch(), suggest() |
search |
Full-text and semantic search | basic(), advanced(), basic_all() |
hierarchy |
Navigate concept relationships | ancestors(), descendants() |
mappings |
Cross-vocabulary mappings | get(), map() |
vocabularies |
Vocabulary metadata | list(), get(), stats() |
domains |
Domain information | list(), get(), concepts() |
# Automatic pagination - fetch all results
all_results <- client$search$basic_all("diabetes", page_size = 100)
# Manual pagination
page1 <- client$search$basic("diabetes", page = 1, page_size = 20)
page2 <- client$search$basic("diabetes", page = 2, page_size = 20)# Specify vocabulary version
client <- OMOPHubClient$new(vocab_version = "2025.2")
# Custom configuration
client <- OMOPHubClient$new(
api_key = "oh_xxx",
base_url = "https://api.omophub.com/v1",
timeout = 30
)tryCatch({
result <- client$concepts$get(999999999)
}, omophub_not_found_error = function(e) {
message("Concept not found: ", conditionMessage(e))
}, omophub_auth_error = function(e) {
message("Check your API key")
}, omophub_rate_limit_error = function(e) {
message("Rate limited, please wait")
}, omophub_api_error = function(e) {
message("API error: ", conditionMessage(e))
})| Feature | OMOPHub SDK | ATHENA Download | OHDSI WebAPI |
|---|---|---|---|
| Setup time | 1 minute | Hours | Hours |
| Infrastructure | None | PostgreSQL required | Full OHDSI stack |
| Updates | Automatic | Manual download | Manual |
| Programmatic access | Native R | SQL/DatabaseConnector | REST API |
Best for: R users who need quick, programmatic access to OMOP vocabularies without infrastructure overhead.
The package includes comprehensive examples in inst/examples/:
| Example | Description |
|---|---|
basic_usage.R |
Getting started - client setup, concept lookup, search |
search_concepts.R |
Search capabilities - filters, autocomplete, pagination |
navigate_hierarchy.R |
Hierarchy navigation - ancestors, descendants |
map_between_vocabularies.R |
Cross-vocabulary mapping |
error_handling.R |
Error handling patterns |
Run an example:
example_path <- system.file("examples", "basic_usage.R", package = "omophub")
source(example_path)We welcome contributions!
# Clone and install for development
# install.packages("devtools")
devtools::install_github("omopHub/omophub-R")
# Run tests
devtools::test()
# Check package
devtools::check()- GitHub Issues
- GitHub Discussions
- Email: support@omophub.com
- Website: omophub.com
MIT License - see LICENSE for details.
Built for the OHDSI community