Getting Started with omophub

Authentication

The package requires an API key from OMOPHub.

Set your API key as an environment variable:

Sys.setenv(OMOPHUB_API_KEY = "your_api_key_here")

Or add it to your .Renviron file for persistence:

OMOPHUB_API_KEY=your_api_key_here

Creating a Client

library(omophub)

# Create client (reads API key from environment)
client <- OMOPHubClient$new()

# Or provide API key explicitly
client <- OMOPHubClient$new(api_key = "your_api_key")

# With additional options
client <- OMOPHubClient$new(
  api_key = "your_api_key",
  timeout = 30,
  max_retries = 3,
  vocab_version = "2025.1"
)

Getting Concepts

Retrieve a concept by its OMOP concept ID:

concept <- client$concepts$get(201826)
print(concept$concept_name)
# [1] "Type 2 diabetes mellitus"

Get a concept by vocabulary-specific code:

concept <- client$concepts$get_by_code("SNOMED", "44054006")
print(concept$concept_name)
# [1] "Type 2 diabetes mellitus"

Batch Operations

Retrieve multiple concepts in a single request:

result <- client$concepts$batch(c(201826, 4329847, 1112807))
for (concept in result$concepts) {
  cat(sprintf("%s: %s\n", concept$concept_id, concept$concept_name))
}

Searching Concepts

Basic search:

results <- client$search$basic("diabetes mellitus", page_size = 10)
for (concept in results$data) {
  cat(sprintf("%s: %s\n", concept$concept_id, concept$concept_name))
}

Search with filters:

results <- client$search$basic(
  "heart attack",
  vocabulary_ids = "SNOMED",
  domain_ids = "Condition",
  page_size = 20
)

Semantic Search

Search using natural language queries powered by neural embeddings:

# Natural language search - understands clinical intent
results <- client$search$semantic("high blood sugar levels")
for (r in results$data$results) {
  cat(sprintf("%s (similarity: %.2f)\n", r$concept_name, r$similarity_score))
}

Filter semantic search results:

results <- client$search$semantic(
  "heart attack",
  vocabulary_ids = "SNOMED",
  domain_ids = "Condition",
  threshold = 0.5
)

Fetch all semantic search results with automatic pagination:

all_results <- client$search$semantic_all(
  "chronic kidney disease",
  page_size = 50,
  max_pages = 5,
  progress = TRUE
)
print(nrow(all_results))

Similarity Search

Find concepts similar to a reference concept:

# Find concepts similar to Type 2 diabetes mellitus
similar <- client$search$similar(concept_id = 201826)
for (s in similar$similar_concepts) {
  cat(sprintf("%s (score: %.2f)\n", s$concept_name, s$similarity_score))
}

Search by natural language query with different algorithms:

# Semantic similarity (neural embeddings)
similar <- client$search$similar(
  query = "high blood pressure",
  algorithm = "semantic"
)

# Lexical similarity (string matching)
similar <- client$search$similar(
  query = "high blood pressure",
  algorithm = "lexical"
)

# Hybrid (combined - default)
similar <- client$search$similar(
  query = "high blood pressure",
  algorithm = "hybrid",
  include_scores = TRUE,
  include_explanations = TRUE
)

Bulk Search

Search for multiple queries in a single API call — much faster than individual requests when you have many terms to look up.

Bulk Lexical Search

Execute up to 50 keyword searches at once:

results <- client$search$bulk_basic(list(
  list(search_id = "q1", query = "diabetes mellitus"),
  list(search_id = "q2", query = "hypertension"),
  list(search_id = "q3", query = "aspirin")
), defaults = list(vocabulary_ids = list("SNOMED"), page_size = 5))

# Each result is matched by search_id
for (item in results$results) {
  cat(sprintf("%s: %d results\n", item$search_id, length(item$results)))
}

Bulk Semantic Search

Execute up to 25 natural-language searches using neural embeddings:

results <- client$search$bulk_semantic(list(
  list(search_id = "s1", query = "heart failure treatment options"),
  list(search_id = "s2", query = "type 2 diabetes medication")
), defaults = list(threshold = 0.5, page_size = 10))

for (item in results$results) {
  cat(sprintf("%s: %d results\n", item$search_id,
              item$result_count %||% length(item$results)))
}

Defaults apply to all searches; per-search values override them:

# Different domains per query, shared vocabulary filter
results <- client$search$bulk_basic(list(
  list(search_id = "cond", query = "diabetes", domain_ids = list("Condition")),
  list(search_id = "drug", query = "metformin", domain_ids = list("Drug"))
), defaults = list(vocabulary_ids = list("SNOMED", "RxNorm"), page_size = 5))

Autocomplete

Get suggestions for autocomplete:

suggestions <- client$concepts$suggest("diab", page_size = 5)
for (s in suggestions$suggestions) {
  print(s$suggestion)
}

Pagination

Manual Pagination

# First page
results <- client$search$basic("diabetes", page = 1, page_size = 50)

# Check pagination info
print(results$meta$total_items)
print(results$meta$has_next)

# Get next page if available
if (isTRUE(results$meta$has_next)) {
  page2 <- client$search$basic("diabetes", page = 2, page_size = 50)
}

Automatic Pagination

Fetch all results as a tibble:

all_results <- client$search$basic_all(
  "diabetes",
  page_size = 100,
  max_pages = 5,
  progress = TRUE
)

# Results are a tibble
print(nrow(all_results))
print(names(all_results))

Hierarchy Navigation

Get ancestors (parent concepts):

result <- client$hierarchy$ancestors(201826, max_levels = 3)
for (ancestor in result$ancestors) {
  print(ancestor$concept_name)
}

Get descendants (child concepts):

result <- client$hierarchy$descendants(201826, max_levels = 2)
for (descendant in result$descendants) {
  print(descendant$concept_name)
}

Concept Mappings

Find how a concept maps to other vocabularies:

result <- client$mappings$get(201826)
for (mapping in result$mappings) {
  cat(sprintf("%s: %s\n",
              mapping$target_vocabulary_id,
              mapping$target_concept_name))
}

Map to specific vocabularies:

result <- client$mappings$get(
  201826,
  target_vocabulary = "ICD10CM"
)

Error Handling

Use tryCatch to handle errors:

tryCatch(
  {
    concept <- client$concepts$get(999999999)
  },
  omophub_not_found = function(e) {
    message("Concept not found: ", e$message)
  },
  omophub_api_error = function(e) {
    message("API error: ", e$message)
  }
)

FHIR-to-OMOP Resolution

The FHIR resolver translates FHIR coded values to OMOP standard concepts in a single call — handling URI mapping, code lookup, Maps to traversal, and CDM target table assignment automatically.

Single Coding

result <- client$fhir$resolve(
  system = "http://snomed.info/sct",
  code = "44054006",
  resource_type = "Condition"
)
cat(result$resolution$standard_concept$concept_name)
cat(result$resolution$target_table)   # "condition_occurrence"
cat(result$resolution$mapping_type)   # "direct"

Non-Standard Code (Automatic Maps-to Traversal)

result <- client$fhir$resolve(
  system = "http://hl7.org/fhir/sid/icd-10-cm",
  code = "E11.9"
)
cat(result$resolution$mapping_type)                    # "mapped"
cat(result$resolution$standard_concept$vocabulary_id)  # "SNOMED"

Batch Resolution

batch <- client$fhir$resolve_batch(list(
  list(system = "http://snomed.info/sct", code = "44054006"),
  list(system = "http://loinc.org", code = "2339-0")
))
cat(sprintf("Resolved: %d/%d\n", batch$summary$resolved, batch$summary$total))

CodeableConcept with Vocabulary Preference

result <- client$fhir$resolve_codeable_concept(
  coding = list(
    list(system = "http://snomed.info/sct", code = "44054006"),
    list(system = "http://hl7.org/fhir/sid/icd-10-cm", code = "E11.9")
  ),
  resource_type = "Condition"
)
# SNOMED wins over ICD-10-CM per OHDSI preference
cat(result$best_match$resolution$source_concept$vocabulary_id)  # "SNOMED"