Exploring OMOP concepts with R

Last updated on 2025-12-03 | Edit this page

Overview

Questions

Find the vocabulary and domain for a given concept_id
Establish whether a concept_id is a standard concept
Find all concepts within a given domain
Find all concepts within a given vocabulary

Objectives

Understand that concepts have additional attributes such as vocabulary, domain, and standard concept status
Use R to query the concept table for specific attributes of concepts
Filter concepts based on domain and vocabulary
Identify standard concepts within the OMOP vocabulary

Introduction

Nearly everything in a hospital can be represented by an OMOP concept_id.

Any column within the OMOP CDM named *concept_id contains OMOP concept IDs. An OMOP concept_id is a unique integer identifier. Concept_ids are defined in the OMOP concept table where a corresponding name and other attributes are stored. OMOP contains concept_ids for other medical vocabularies such as SNOMED and LOINC, which OMOP terms as source vocabularies.

Looking up OMOP concepts

OMOP concepts can be looked up in Athena an online tool provided by OHDSI.

The CDMConnector package allows connection to an OMOP Common Data Model in a database. It also contains synthetic example data that can be used to demonstrate querying the data.

In the previous lesson we set up the CDMConnector package to connect to an OMOP Common Data Model database and used it to look at the concepts table. We also created the function get_concept_name() to get a humanly readable name for a concept_id. ### Setting up the connection

R

library(CDMConnector)

db_name <- "GiBleed"
CDMConnector::requireEunomia(datasetName = db_name)

OUTPUT


Download completed!

R

db <- DBI::dbConnect(duckdb::duckdb(),
                     dbdir = CDMConnector::eunomiaDir(datasetName = db_name))

cdm <- CDMConnector::cdmFromCon(con = db, cdmSchema = "main",
                                writeSchema = "main")

Exploring the concept table

R

colnames(cdm$concept)

OUTPUT

 [1] "concept_id"       "concept_name"     "domain_id"        "vocabulary_id"
 [5] "concept_class_id" "standard_concept" "concept_code"     "valid_start_date"
 [9] "valid_end_date"   "invalid_reason"

concept table columns	Description
concept_id	Unique identifier for the concept.
concept_name	Name or description of the concept.
domain_id	The domain to which the concept belongs (e.g. Condition, Drug).
vocabulary_id	The vocabulary from which the concept originates (e.g. SNOMED, RxNorm).
concept_class_id	Classification within the vocabulary (e.g. Clinical Finding, Ingredient).
standard_concept	‘S’ for standard concepts that can be included in network studies
concept_code	Code used by the source vocabulary to identify the concept.
valid_start_date	Date the concept became valid in OMOP.
valid_end_date	Date the concept ceased to be valid.
invalid_reason	Reason for invalidation, if applicable

The concept table is the main table for looking up information about concepts. We can use R to query the concept table for specific attributes of concepts.

Challenge

Answer the following questions using R and the concept table:

How many entries are there in the concept table?
How many distinct vocabularies are there in the concept table?
How many distinct domains other than ‘None’ are there in the concept table?

Show me the solution

R

# 1. How many entries are there in the concept table?
cdm$concept %>% summarise(n_concepts = n())

ERROR

Error in summarise(., n_concepts = n()): could not find function "summarise"

Answer: There are 444 entries in the concept table. This is a tiny fraction of the overall table which can be found at Athena

R

# 2. How many distinct vocabularies are there in the concept table?
cdm$concept %>% summarise(n_distinct_vocabularies = n_distinct(vocabulary_id))

ERROR

Error in summarise(., n_distinct_vocabularies = n_distinct(vocabulary_id)): could not find function "summarise"

Answer: There are 9 distinct vocabularies used in this dataset.

R

# 3. How many distinct domains other than 'None' are there in the concept table?
cdm$concept %>%
  filter(domain_id != "None") %>%
  summarise(n_distinct_domains = n_distinct(domain_id))

ERROR

Error in summarise(., n_distinct_domains = n_distinct(domain_id)): could not find function "summarise"

Answer: There are 8 distinct domains other than ‘None’ in this dataset.

Filtering concepts by domain, vocabulary and standard concept status

Let’s look into filtering concepts based on their domain and vocabulary.

Challenge

Lists the first ten rows of the concept table, listing concept_id, domain_id, vocabulary_id and standard_concept columns.

Show me the solution

R

cdm$concept |>
  arrange(concept_id) |>
  filter(row_number() <= 10) |>
  select(concept_id, domain_id, vocabulary_id, standard_concept) |>
  collect()

ERROR

Error in collect(select(filter(arrange(cdm$concept, concept_id), row_number() <= : could not find function "collect"

Note we have to use collect() to pull the data into R memory to view it.

Look at vocabularies

Vocabulary: The source or system of coding for concepts, such as SNOMED, RxNorm, LOINC, or ICD‑10. OMOP maps many vocabularies into a common, standardised set so different coding systems can be analysed together.

Challenge

List all distinct vocabularies in the concept table.

Show me the solution

R

cdm$concept |>
  filter(!is.na(vocabulary_id)) |>
  distinct(vocabulary_id) |>
  arrange(vocabulary_id) |>
  pull(vocabulary_id)

ERROR

Error in pull(arrange(distinct(filter(cdm$concept, !is.na(vocabulary_id)), : could not find function "pull"

Here we can use pull(x) to pull the data x into R memory to view it.

Look at domains

Domain: A high‑level category that groups concepts by what they represent in clinical data, such as Condition, Drug, Procedure, Measurement, or Observation. A concept’s domain determines which OMOP table it belongs to and how it’s used analytically.

Challenge

List all distinct domains in the concept table.

Show me the solution

R

cdm$concept |>
  filter(!is.na(domain_id)) |>
  distinct(domain_id) |>
  arrange(domain_id) |>
  pull(domain_id)

ERROR

Error in pull(arrange(distinct(filter(cdm$concept, !is.na(domain_id)), : could not find function "pull"

Look at non standard concepts

A standard concept is the preferred, harmonised code in OMOP that represents a clinical idea across vocabularies. Standard concepts (standard_concept = “S”) are the target of mappings from source codes, and they define which domain and table the data belong to for consistent analysis.

Challenge

Find any nonstandard concepts (i.e. concepts where standard_concept is not ‘S’) by filtering the concept table. List the first 10 concept_ids of nonstandard concepts. Then look up their concept_name, domain_id, vocabulary_id and standard_concept status.

Show me the solution

R

cdm$concept |>
  filter(is.na(standard_concept) | standard_concept != "S") |>
  slice_min(order_by = concept_id, n = 10, with_ties = FALSE) |>
  pull(concept_id)

ERROR

Error in pull(slice_min(filter(cdm$concept, is.na(standard_concept) | : could not find function "pull"

R

cdm$concept |>
  filter(concept_id %in% c(1569708, 35208414, 44923712, 45011828)) |>
  select(concept_id, concept_name, domain_id, vocabulary_id, standard_concept) |>
  collect()

ERROR

Error in collect(select(filter(cdm$concept, concept_id %in% c(1569708, : could not find function "collect"

Now you should be able to replicate our get_concept_name() function to look up other attributes of concepts such as domain, vocabulary and standard concept status.

Challenge

Questions?

Find the domain and vocabulary for concept_id 35208414

Is this concept a standard concept?

Show me the solution

R

library(dplyr)
get_concept_domain <- function(id) {
  cdm$concept |>
    filter(concept_id == !!id) |>
    select(domain_id) |>
    pull()
}

get_concept_vocabulary <- function(id) {
  cdm$concept |>
    filter(concept_id == !!id) |>
    select(vocabulary_id) |>
    pull()
}

get_concept_standard_status <- function(id) {
  cdm$concept |>
    filter(concept_id == !!id) |>
    select(standard_concept) |>
    pull()
}

get_concept_domain(35208414)

OUTPUT

[1] "Condition"

R

get_concept_vocabulary(35208414)

OUTPUT

[1] "ICD10CM"

R

get_concept_standard_status(35208414)

OUTPUT

[1] NA

Answer:

The domain for concept_id 319835 is ‘Condition’.
The vocabulary for concept_id 319835 is ‘ICD10CM’.
This concept is not a standard concept (standard_concept = ‘NA’).

Key Points

Concepts have additional attributes such as vocabulary, domain, and standard concept status
The concept table can be queried using R to retrieve specific attributes of concepts
Concepts can be filtered based on their domain and vocabulary
Standard concepts are those that are recommended for use in analyses within the OMOP framework