# Getting Started with omophub

## Introduction

The omophub package provides an R interface to the OMOPHub API for accessing OHDSI ATHENA standardized medical vocabularies. This vignette demonstrates basic usage patterns.

## Installation

Install from GitHub:

    # install.packages("devtools")
    devtools::install_github("omophub/omophub-R")

## 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"

### Tibble Output for Batch Resolution

For `dplyr` / `tidyr` workflows, pass `as_tibble = TRUE` to get a flat tibble with one row per input coding and columns for the source and standard concepts, target CDM table, mapping type, and resolution status. This is the most ergonomic shape for ETL pipelines processing many codes:

    library(dplyr)

    tbl <- client$fhir$resolve_batch(
      list(
        list(system = "http://hl7.org/fhir/sid/icd-10-cm", code = "E11.9"),
        list(system = "http://hl7.org/fhir/sid/icd-10-cm", code = "I10"),
        list(system = "http://hl7.org/fhir/sid/icd-10-cm", code = "J45.909")
      ),
      as_tibble = TRUE
    )

    tbl |>
      filter(status == "resolved") |>
      select(source_code, standard_concept_name, target_table)

Failed rows are kept in-place with `status = "failed"` and the error text in `status_detail` — you can filter them out rather than silently dropping them. The batch summary (`total` / `resolved` / `failed`) is attached as an attribute:

    attr(tbl, "summary")

The default `as_tibble = FALSE` still returns the list-shaped `list(results, summary)`, so existing code keeps working unchanged.

### Standalone Wrapper Functions

For pipe-friendly workflows, `omophub` also exports standalone wrapper functions that take the client as their first argument. Both forms are fully supported — pick whichever reads better for the surrounding code:

    # Equivalent to client$fhir$resolve()
    client |>
      fhir_resolve(
        system = "http://snomed.info/sct",
        code = "44054006",
        resource_type = "Condition"
      )

    # Tibble-shaped batch in a pipe
    tbl <- client |>
      fhir_resolve_batch(
        codings = list(
          list(system = "http://snomed.info/sct", code = "44054006"),
          list(system = "http://loinc.org", code = "2339-0")
        ),
        as_tibble = TRUE
      )

    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"
      )

### FHIR Client Interop with `omophub_fhir_url()`

When you need raw FHIR `Parameters` / `Bundle` responses instead of the Concept Resolver envelope, `omophub_fhir_url()` returns the OMOPHub FHIR Terminology Service base URL so you can talk to it directly with `httr2` or `fhircrackr`. Supports FHIR versions `"r4"` (default), `"r4b"`, `"r5"`, and `"r6"`:

    omophub_fhir_url()
    #> "https://fhir.omophub.com/fhir/r4"

    omophub_fhir_url("r5")
    #> "https://fhir.omophub.com/fhir/r5"

Example: call `CodeSystem/$lookup` directly with `httr2`:

    library(httr2)

    resp <- request(omophub_fhir_url()) |>
      req_url_path_append("CodeSystem/$lookup") |>
      req_url_query(
        system = "http://snomed.info/sct",
        code = "44054006"
      ) |>
      req_headers(Authorization = paste("Bearer", Sys.getenv("OMOPHUB_API_KEY"))) |>
      req_perform()

    params <- resp_body_json(resp)
    # Raw FHIR Parameters resource with the concept display and designations.

Use `client$fhir$resolve()` (or `fhir_resolve()`) when you want OMOP-enriched answers (standard concept, CDM target table, mapping quality). Use `omophub_fhir_url()` + `httr2` when you need FHIR-native responses for FHIR-native tooling.

## Further Resources

- OMOPHub API Documentation
- Package Documentation
- GitHub Repository