Active development 2024, breaking changes possible
omopcept provides access to OMOP conCEPTs (all pros, no cons!).
Initial motivation was to make it super-easy to get the names associated
with concept IDs.
Later additions allow exploration and visualisation of OMOP hierarchies.
omopcept provides R functions that are :
- modern
- flexible
- tidyverse compatible
- memory efficient (using arrow parquet)
omopcept includes concise named copies of functions designed for
interactive use e.g. oid()
and onames()
to search concept ids and
names respectively. For example the line below can be used to return all
~ 1000 OMOP ids for SNOMED codes for clinical drugs starting with A.
onames("^a",d="DRUG",v="SNOMED",cc="Clinical Drug")
Install the development version of omopcept with:
# install.packages("remotes")
remotes::install_github("andysouth/omopcept")
OMOP vocabularies can be searched and downloaded from Athena – the OHDSI vocabularies repository. omopcept provides R tools to interact with OMOP concepts in a more reproducible way.
omopcept can use vocabulary files that you have downloaded from Athena, or automatically download a subset of the vocabularies that we have saved in the cloud.
On initial use omopcept tries to download OMOP vocabulary files from the cloud to a local package cache where it can be accessed in future sessions. The arrow R package allows parquet files to be opened and queried in dplyr pipelines without having to read in the data. e.g. the code below will return just the top rows of the concept table.
library(omopcept)
omop_concept() |>
head() |>
dplyr::collect()
full name | quick interactive name | action |
---|---|---|
omop_names() |
onames() |
search concepts by parts of names |
omop_id() |
oid() |
search for concept_id(s) |
omop_domain() |
- | return domain for concept_id(s) |
omop_join_name() |
ojoin() |
join an omop name column onto a table with an id column |
omop_join_name_all() |
ojoinall() |
join omop names columns onto all id columns in a table |
omop_check_names() |
ochecknames() |
check that names match ids |
omop_ancestors() |
oance() |
return ancestors of a concept |
omop_descendants() |
odesc() |
return descendants of a concept |
omop_relations() |
orels() |
return (immediate) relations of a concept including the nature of the relationship e.g. ‘Is a’ |
omop_relations_recursive() |
- | return (immediate) relations of a concept and the relations of those up to num_recurse |
omop_graph() |
- | graph omop relationships (experimental) |
omop_concept() |
oc() |
return reference to concept table (for use in dplyr pipelines) |
omop_concept_ancestor() |
oca() |
return reference to concept ancestor table |
omop_concept_relationship() |
ocr() |
return reference to concept relationship table |
omop_concept_fields() |
ocfields() |
names of concept table columns |
omop_concept_ancestor_fields() |
ocafields() |
names of concept ancestor table columns |
omop_concept_relationship_fields() |
ocrfields() |
names of concept relationship table columns |
The OMOP Common Data Model is an open standard for health data. “[It is] designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence”.
OMOP is maintained by OHDSI (pronounced “Odyssey”). “The Observational Health Data Sciences and Informatics program is a multi-stakeholder, interdisciplinary collaborative that strives to improve medical decision making and bring better health outcomes to patients around the world.”
Vocabularies downloaded from Athena include tables called CONCEPT.csv, CONCEPT_ANCESTOR.csv and CONCEPT_RELATIONSHIP.csv.
You have two main options :
-
manually download selected vocabulary csv files from Athena, use
omopcept::omop_vocabs_preprocess()
-
automatically download pre-processed vocabulary files saved in the cloud by us
omopcept downloads a selection of vocabularies and stores them locally the first time you use it (in the recommended data location for R packages). The download does not need to be repeated until you update the package. Vocabularies are stored as parquet files that can be queried in a memory-efficient manner without having to first read the data in to memory.
fields | about | query_arguments |
---|---|---|
concept_id | unique id | c_ids |
concept_name | descriptive name | pattern |
domain_id | e.g. drug, measurement | d_ids |
vocabulary_id | e.g. LOINC, SNOMED | v_ids |
concept_class_id | e.g. Clinical Observation, Organism | cc_ids |
standard_concept | standard or not | standard |
concept_code | source code | |
valid_start_date | ||
valid_end_date | ||
invalid_reason |
omop_names("chemotherapy", v_ids="LOINC")
#> # A tibble: 71 × 10
#> concept_id concept_name domain_id vocabulary_id concept_class_id
#> <int> <chr> <chr> <chr> <chr>
#> 1 3010410 Chemotherapy records Observat… LOINC Clinical Observ…
#> 2 3002377 Chemotherapy treatment a… Measurem… LOINC Clinical Observ…
#> 3 3011998 Date 1st chemotherapy tr… Observat… LOINC Clinical Observ…
#> 4 3003037 Chemotherapy treatment C… Measurem… LOINC Clinical Observ…
#> 5 3000897 Reason for no chemothera… Measurem… LOINC Clinical Observ…
#> 6 3014397 Chemotherapy Cancer Measurem… LOINC Clinical Observ…
#> 7 3027104 Chemotherapy treatment C… Measurem… LOINC Clinical Observ…
#> 8 3037369 2nd course chemotherapy … Measurem… LOINC Clinical Observ…
#> 9 3032293 3rd course chemotherapy … Measurem… LOINC Clinical Observ…
#> 10 3028808 4th course chemotherapy … Measurem… LOINC Clinical Observ…
#> # ℹ 61 more rows
#> # ℹ 5 more variables: standard_concept <chr>, concept_code <chr>,
#> # valid_start_date <date>, valid_end_date <date>, invalid_reason <chr>
omop_names("chemotherapy", v_ids=c("LOINC","SNOMED"), d_ids=c("Observation","Procedure"))
#> # A tibble: 297 × 10
#> concept_id concept_name domain_id vocabulary_id concept_class_id
#> <int> <chr> <chr> <chr> <chr>
#> 1 3010410 Chemotherapy records Observat… LOINC Clinical Observ…
#> 2 3011998 Date 1st chemotherapy tr… Observat… LOINC Clinical Observ…
#> 3 3046488 Chemotherapy [Minimum Da… Observat… LOINC Survey
#> 4 40758122 Chemotherapy in last 14 … Observat… LOINC Survey
#> 5 40758123 Chemotherapy in last 14 … Observat… LOINC Survey
#> 6 40766658 Type of chemotherapy [Ph… Observat… LOINC Clinical Observ…
#> 7 40768860 Cancer chemotherapy rece… Observat… LOINC Clinical Observ…
#> 8 40770073 Have you been treated wi… Observat… LOINC Clinical Observ…
#> 9 40770096 History of Chemotherapy … Observat… LOINC Clinical Observ…
#> 10 36305649 Chemotherapy infusion st… Observat… LOINC Clinical Observ…
#> # ℹ 287 more rows
#> # ℹ 5 more variables: standard_concept <chr>, concept_code <chr>,
#> # valid_start_date <date>, valid_end_date <date>, invalid_reason <chr>
Helps to interpret OMOP data.
data.frame(concept_id=(c(3571338L,4002075L))) |>
omop_join_name()
#> # A tibble: 2 × 2
#> concept_id concept_name
#> <int> <chr>
#> 1 3571338 Problem behaviour
#> 2 4002075 BLUE LOTION
data.frame(drug_concept_id=(c(4000794L,4002592L))) |>
omop_join_name(namestart="drug")
#> # A tibble: 2 × 2
#> drug_concept_id drug_concept_name
#> <int> <chr>
#> 1 4000794 BUZZ OFF
#> 2 4002592 DEXAMETHASONE INJECTION
data.frame(concept_id=(c(3571338L,3655355L)),
drug_concept_id=(c(4000794L,35628998L))) |>
omop_join_name_all()
#> # A tibble: 2 × 4
#> concept_id concept_name drug_concept_id drug_concept_name
#> <int> <chr> <int> <chr>
#> 1 3571338 Problem behaviour 4000794 BUZZ OFF
#> 2 3655355 Erectile dysfunction 35628998 Viagra
relations <- omop_relations_recursive(4055049L,
r_ids=c('Is a','Subsumes'),
num_recurse=2)
omop_graph(relations, saveplot=FALSE, graphtitle=NULL, legendshow=FALSE, nodetxtsize=5)
The vocabularies that omopcept downloads automatically are a default download from Athena with a few extra vocabs added. If you wish to control which vocabularies are included you can manually download vocabulary csv files from Athena.
library(dplyr)
library(ggplot2)
library(forcats)
concept_summary <-
omop_concept() |>
count(vocabulary_id, sort=TRUE) |>
collect()
ggplot(concept_summary,aes(y=reorder(vocabulary_id,n),x=n,col=vocabulary_id)) +
geom_point() +
labs(y = "vocabulary_id") +
guides(col="none") +
theme_minimal()