Exploring OMOP concepts with R

Last updated on 2026-02-08 | Edit this page

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Overview

Questions

Find the vocabulary, domain and concept_class 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, classand standard concept status
Use R to query the concept table for specific attributes of concepts
Filter concepts based on domain, vocabulary and class
Identify standard concepts within the OMOP vocabulary

Introduction

The primary purpose of the concept table is to provide a standardised representation of medical Concepts, allowing for consistent querying and analysis across healthcare databases. Users can join the concept table with other tables in the CDM to enrich clinical data with Concept information or use the concept table as a reference for mapping clinical data from source terminologies to Standard or other Concepts.

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.

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

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 episode 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. We will use these again in this episode.

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 come from internationally accepted standard vocabularies.
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?
How many distinct concept_classes are there in the concept table?

Show me the solution

How many entries are there in the concept table?

R

library(dplyr)
cdm$concept |>
  summarise(n_concepts = n())

OUTPUT

# Source:   SQL [?? x 1]
# Database: DuckDB 1.4.1 [unknown@Linux 6.8.0-1044-azure:R 4.5.2//tmp/Rtmpd8tQ6t/file17767ba676cb.duckdb]
  n_concepts
       <dbl>
1        444

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

CODING_NOTE: The function n() counts the number of rows in the table and summarise() creates a summary table with that count. These functions are part of the dplyr package. When you have loaded the library once your environment will remember it for the rest of the session.

How many distinct vocabularies are there in the concept table?

R

cdm$concept |>
  summarise(n_distinct_vocabularies = n_distinct(vocabulary_id))

OUTPUT

# Source:   SQL [?? x 1]
# Database: DuckDB 1.4.1 [unknown@Linux 6.8.0-1044-azure:R 4.5.2//tmp/Rtmpd8tQ6t/file17767ba676cb.duckdb]
  n_distinct_vocabularies
                    <dbl>
1                       9

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

CODING_NOTE: The function n_distinct(x) counts the number of distinct values in the column x.

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

R

cdm$concept |>
  filter(domain_id != "None") |>
  summarise(n_distinct_domains = n_distinct(domain_id))

OUTPUT

# Source:   SQL [?? x 1]
# Database: DuckDB 1.4.1 [unknown@Linux 6.8.0-1044-azure:R 4.5.2//tmp/Rtmpd8tQ6t/file17767ba676cb.duckdb]
  n_distinct_domains
               <dbl>
1                  8

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

CODING_NOTE: We use the filter() function to filter out rows where the domain_id is ‘None’ before counting the distinct domains.

How many distinct concept_classes are there in the concept table?

R

cdm$concept |>
  summarise(n_distinct_concept_classes = n_distinct(concept_class_id))

OUTPUT

# Source:   SQL [?? x 1]
# Database: DuckDB 1.4.1 [unknown@Linux 6.8.0-1044-azure:R 4.5.2//tmp/Rtmpd8tQ6t/file17767ba676cb.duckdb]
  n_distinct_concept_classes
                       <dbl>
1                         21

Answer: There are 21 distinct concept_classes used in this dataset.

Filtering concepts by domain, vocabulary, class and standard concept status

Let’s look into filtering concepts based on their domain, vocabulary, concept_class and standard_concept status.

Challenge

List the first ten rows of the concept table, listing only the concept_id, domain_id, vocabulary_id, concept_class_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, concept_class_id, standard_concept) |>
  collect()

OUTPUT

# A tibble: 10 × 5
   concept_id domain_id vocabulary_id concept_class_id standard_concept
        <int> <chr>     <chr>         <chr>            <chr>
 1          0 Metadata  None          Undefined        <NA>
 2       8507 Gender    Gender        Gender           S
 3       8532 Gender    Gender        Gender           S
 4       9201 Visit     Visit         Visit            S
 5       9202 Visit     Visit         Visit            S
 6       9203 Visit     Visit         Visit            S
 7      28060 Condition SNOMED        Clinical Finding S
 8      30753 Condition SNOMED        Clinical Finding S
 9      78272 Condition SNOMED        Clinical Finding S
10      80180 Condition SNOMED        Clinical Finding S

CODING_NOTE: The arrange() function orders the rows by concept_id. The filter(row_number() <= 10) function filters to the first 10 rows. The select() function selects only the specified columns. We have to use collect() to pull the data into R memory to view it. This is because we are querying a remote database, not one that is local..

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)

OUTPUT

[1] "ICD10CM" "LOINC"   "NDC"     "Visit"   "Gender"  "RxNorm"  "CVX"
[8] "SNOMED"  "None"

CODING_NOTE: Here we can use pull(x) to pull the data x into R memory to view it. This is because we are only requiring one column of data, so we can pull that column directly into R memory without needing to use collect() first.

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)

OUTPUT

[1] "Drug"        "Measurement" "Condition"   "Procedure"   "Observation"
[6] "Visit"       "Metadata"    "Gender"

Look at concept classes

Class: A lower level category that groups concepts within a domain by what they represent in clinical data.

Challenge

List all distinct concept_classes in the concept table.

Show me the solution

R

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

OUTPUT

 [1] "Branded Drug"         "3-char nonbill code"  "Quant Branded Drug"
 [4] "Branded Drug Comp"    "Visit"                "Context-dependent"
 [7] "Undefined"            "Morph Abnormality"    "4-char billing code"
[10] "Procedure"            "Lab Test"             "Clinical Drug"
[13] "Clinical Finding"     "Clinical Observation" "Quant Clinical Drug"
[16] "CVX"                  "Ingredient"           "11-digit NDC"
[19] "Branded Pack"         "Clinical Drug Comp"   "Gender"

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. However, OMOP also include nonstandard concepts from sources that are not globally used but maybe useful locally. dm+d, the NHS Dictionary of Medicines and Devices is one such vocabulary that is included in OMOP but is not a standard vocabulary.

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)

OUTPUT

[1]        0  1569708 35208414 44923712 45011828

Answer: There are only four nonstandard concepts in this dataset: 1569708, 35208414, 44923712, 45011828.

CODING_NOTE: We use slice_min() to get the first 10 rows which match the filter, ordered by concept_id.

R

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

OUTPUT

# A tibble: 4 × 5
  concept_id concept_name               domain_id vocabulary_id standard_concept
       <int> <chr>                      <chr>     <chr>         <chr>
1   35208414 Gastrointestinal hemorrha… Condition ICD10CM       <NA>
2   44923712 celecoxib 200 MG Oral Cap… Drug      NDC           <NA>
3    1569708 Other diseases of digesti… Condition ICD10CM       <NA>
4   45011828 Diclofenac Sodium 75 MG D… Drug      NDC           <NA>

CODING_NOTE: We use %in% to filter for multiple concept_ids which we can list in a vector.

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

Find the domain,vocabulary and concept class 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_concept_class <- function(id) {
  cdm$concept |>
    filter(concept_id == !!id) |>
    select(concept_class_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_concept_class(35208414)

OUTPUT

[1] "4-char billing code"

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’.
The concept class for concept id 319835 is ‘4-char billing code’
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, vocabulary and class
Standard concepts are those that are recommended for use in analyses within the OMOP framework

Exploring OMOP concepts with R

Overview

Questions

Objectives

Introduction

Looking up OMOP concepts

Setting up the connection

R

OUTPUT

R

Exploring the concept table

R

OUTPUT

Challenge

Show me the solution

R

OUTPUT

R

OUTPUT

R

OUTPUT

R

OUTPUT

Filtering concepts by domain, vocabulary, class and standard concept status

Challenge

Show me the solution

R

OUTPUT

Look at vocabularies

Challenge

Show me the solution

R

OUTPUT

Look at domains

Challenge

Show me the solution

R

OUTPUT

Look at concept classes

Challenge

Show me the solution

R

OUTPUT

Look at non standard concepts

Challenge

Show me the solution

R

OUTPUT

R

OUTPUT

Challenge

Show me the solution

R

OUTPUT

R

OUTPUT

R

OUTPUT

R

OUTPUT

Exploring the `concept` table