| Title: | Manipulate SNOMED CT Diagnosis Lists |
| Version: | 1.4.0 |
| Description: | Functions and methods for manipulating 'SNOMED CT' concepts. The package contains functions for loading the 'SNOMED CT' release into a convenient R environment, selecting 'SNOMED CT' concepts using regular expressions, and navigating the 'SNOMED CT' ontology. It provides the 'SNOMEDconcept' S3 class for a vector of 'SNOMED CT' concepts (stored as 64-bit integers) and the 'SNOMEDcodelist' S3 class for a table of concepts IDs with descriptions. The package can be used to construct sets of 'SNOMED CT' concepts for research (<doi:10.1093/jamia/ocac158>). For more information about 'SNOMED CT' visit https://www.snomed.org/. |
| License: | GPL-3 |
| Imports: | data.table (≥ 1.12.8), bit64, methods |
| Depends: | readxl, R (≥ 3.5.0) |
| Suggests: | knitr, rmarkdown, testthat |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.1 |
| Author: | Anoop D. Shah 
[aut, cre] |
| Maintainer: | Anoop D. Shah <anoop@doctors.org.uk> |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-01-28 17:06:42 UTC; anoop |
| Repository: | CRAN |
| Date/Publication: | 2025-01-29 08:30:07 UTC |
Rdiagnosislist: A package for manipulating SNOMED CT diagnosis lists
Description
The Rdiagnosislist package makes it easy to load a SNOMED dictionary into R and use the hierarchies to search for concepts and navigate relations between concepts.
Author(s)
Maintainer: Anoop D. Shah anoop@doctors.org.uk (ORCID)
Sample history substitution table from SNOMED CT dictionary
Description
Sample of the SNOMED CT table showing current equivalents for inactive concepts.
Usage
data(HISTORY)
Format
An object of class "data.table"
Details
- OLDCONCEPTID
integer64: concept ID of the inactive concepts
- OLDCONCEPTSTATUS
integer: status of the old concept
- NEWCONCEPTID
integer64: concept ID of the new concept
- NEWCONCEPTSTATUS
integer: status of the new concept
- PATH
character: path
- ISAMBIGUOUS
integer: whether ambiguous
- ITERATIONS
integer: number of iterations
- OLDCONCEPTFSN
character: old concept Fully Specified Name
- OLDCONCEPTFSN_TAGCOUNT
integer: number of tags for old concept
- NEWCONCEPTFSN
integer: new concept Fully Specified Name
- NEWCONCEPTFSN_STATUS
integer: new concept Fully Specified Name status
- TLH_IDENTICALFLAG
integer: whether TLH identical
- FSN_TAGLESSIDENTICALFLAG
integer: whether Fully Specified Names are identical ignoring the tags
- FSN_TAGIDENTICALFLAG
integer: whether Fully Specified Names tags are identical
See Also
Other SNOMEDsample:
QUERY,
SNOMED_RELATIONSHIP
Other sampleSNOMED:
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Create a TEST environment and load the sample dictionaries
TEST <- new.env()
data(CONCEPT, envir = TEST)
data(DESCRIPTION, envir = TEST)
data(RELATIONSHIP, envir = TEST)
data(STATEDRELATIONSHIP, envir = TEST)
data(HISTORY, envir = TEST)
# Show properties of the history table
str(TEST$HISTORY)
Sample manual synonym table to assist in creation of concept database
Description
Sample manual synonym table to assist in creation of concept database
Usage
data(MANUAL_SYNONYMS)
Format
An object of class "data.table"
Details
- synonym
character: Synonym, in lower case if case is unimportant
- snomed
character: SNOMED CT term, in lower case if case is unimportant
- bidirectional
boolean: whether synonym can only ever mean snomed. Not to be used for abbreviations or acronyms.
See Also
createCDB
Examples
data(MANUAL_SYNONYMS)
str(MANUAL_SYNONYMS)
Sample query table from SNOMED CT dictionary
Description
Sample of the SNOMED CT table of ancestor / descendant relationships for inactive concepts.
Usage
data(QUERY)
Format
An object of class "data.table"
Details
- supertypeId
integer64: concept ID of the ancestor (active) concept
- subtypeId
integer64: concept ID of the descendant (inactive) concept
- provenance
integer: provenance of relationship. Provenance = 0 means subsumption is always true. Provenance = 1 means subsumption is usually true (but there is a theoretical risk of false positives). Provenance = 2 means both ancestors and descendents are only approximately known. Provenance = 3 means original code had at least two distinct meanings and all are being returned
See Also
Other SNOMEDsample:
HISTORY,
SNOMED_RELATIONSHIP
Examples
# Create a TEST environment and load the sample dictionaries
TEST <- new.env()
data(CONCEPT, envir = TEST)
data(DESCRIPTION, envir = TEST)
data(RELATIONSHIP, envir = TEST)
data(STATEDRELATIONSHIP, envir = TEST)
data(QUERY, envir = TEST)
# Show properties of the query table
str(TEST$QUERY)
Sample mappings from Read to SNOMED CT
Description
A sample of a mapping table derived from NHS Digital maps. Contains concepts in Read Clinical Terms Version 2 and Clinical Terms Version 3 that map to a set of SNOMED CT concepts, according to a supplied mapping file. The source data are available from the NHS Digital Technology Reference data Update Distribution https://isd.digital.nhs.uk/trud/user/guest/group/0/pack/9/subpack/9/releases.
Usage
data(READMAPS)
Format
An object of class "data.table"
Details
- conceptId
integer64: SNOMED CT conceptId (primary key)
- read2_code
list: character list of 7-character Read V2 codes
- read2_term
list: character list of Read V2 terms
- ctv3_concept
list: character list of CTV3 concept codes
- ctv3_termid
list: character list of CTV3 term description codes
See Also
loadREADMAPS, getMaps
Other sampleSNOMED:
HISTORY,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Show properties of the READMAPS table
data(READMAPS)
str(READMAPS)
Sample concept table from SNOMED CT dictionary
Description
A sample of the SNOMED CT concept table.
Usage
data(CONCEPT)
Format
An object of class "data.table"
Details
- id
integer64: SNOMED CT conceptId (primary key)
- moduleId
integer64: class of SNOMED CT concept (whether it is used for recording information or is a metadata concept)
- definitionStatusId
integer64: 900000000000074008 = primitive concept, 900000000000073002 = defined by conditions
- effectiveTime
IDate: when the concept became active
- active
logical: whether this concept is currently active
See Also
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Show properties of the CONCEPT table
data('CONCEPT')
str(CONCEPT)
Sample description table from SNOMED CT dictionary
Description
A sample of the SNOMED CT description table. Each concept may has a fully specified name and may have any number of synonyms.
Usage
data(DESCRIPTION)
Format
An object of class "data.table"
Details
- id
integer64: description ID
- moduleId
integer64: class of SNOMED CT concept (whether it is used for recording information or is a metadata concept)
- conceptId
integer64: SNOMED CT concept ID
- languageCode
character: 'en' = English
- typeId
integer64: 900000000000013009 = Synonym, 900000000000003001 = Fully Specified Name
- term
character: term description
- caseSignificanceId
integer64: 900000000000020002 = Initial character case sensitive, 900000000000017005 = Whole term case sensitive, 900000000000448009 = Whole term case insensitive
- effectiveTime
IDate: when the concept became active
- active
logical: whether this concept is currently active
See Also
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Show properties of the DESCRIPTION table
data('DESCRIPTION')
str(DESCRIPTION)
Sample extended map table from SNOMED CT dictionary
Description
A sample of the SNOMED CT extended map table, containing maps to ICD-10 and OPCS4.
Usage
data(EXTENDEDMAP)
Format
An object of class "data.table"
Details
- moduleId
integer64: core metadata concept: 449080006 = SNOMED CT to ICD-10 rule-based mapping module, 999000031000000106 = SNOMED CT United Kingdom Edition reference set module
- refsetId
integer64: foundation metadata concept: 447562003 = ICD-10 complex map reference set, 1126441000000105 = Office of Population Censuses and Surveys Classification of Interventions and Procedures Version 4.9 complex map reference set, 999002271000000101 = International Classification of Diseases, Tenth Revision, Fifth Edition, five character code United Kingdom complex map reference set
- referencedComponentId
integer64: SNOMED CT conceptId of the concept mapped
- mapGroup
integer: mapping group
- mapPriority
integer: priority of alternative maps (1 = highest)
- mapRule
character: advice on mapping rule
- mapAdvice
character: mapping advice
- mapTarget
character: target ICD-10 or OPCS4 code. The optional period between the third and fourth character has been removed for consistency.
- mapCategoryId
integer64: foundation metadata concept describing the quality of the map
- effectiveTime
IDate: when the concept became active
- active
logical: whether this concept is currently active
See Also
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Load the dataset and show its properties
data('EXTENDEDMAP')
str(EXTENDEDMAP)
# This EXTENDEDMAP table is part of the sample SNOMED CT dictionary
# Hence this should show the same properties as above
str(sampleSNOMED()$EXTENDEDMAP)
Sample refset table from SNOMED CT dictionary
Description
A sample of the SNOMED CT refset table. This contains SNOMED CT codelists that are used for partiular operational or clinical purposes, and are curated by SNOMED CT. The id column of the refset table is not included, in order to save space.
Usage
data(REFSET)
Format
An object of class "data.table"
Details
- moduleId
integer64: SNOMED CT core metadata concept, stating whether the refset is from the SNOMED CT core module or the UK extension.
- refsetId
integer64: SNOMED CT conceptId of the refset. These concepts have semantic type 'foundation metadata concept'
- referencedComponentId
integer64: SNOMED CT conceptId of the member of the refset
- effectiveTime
IDate: when the concept became active
- active
logical: whether this concept is currently active
See Also
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Load the dataset and show its properties
data('REFSET')
str(REFSET)
# This REFSET table is part of the sample SNOMED CT dictionary
# Hence this should show the same properties as above
str(sampleSNOMED()$REFSET)
Sample relationship tables from SNOMED CT dictionary
Description
Samples of the SNOMED CT tables of stated relationships (RELATIONSHIP) and inferred relationships (RELATIONSHIP).
Usage
data(RELATIONSHIP); data(STATEDRELATIONSHIP)
STATEDRELATIONSHIP
Format
An object of class "data.table"
An object of class data.table (inherits from data.frame) with 329 rows and 10 columns.
Details
- id
integer64: ID of the relationship record (primary key)
- active
logical: whether this concept is currently active
- moduleId
integer64: class of SNOMED CT concept (whether it is used for recording information or is a metadata concept)
- sourceId
integer64: source SNOMED CT concept for the relationship
- destinationId
integer64: destination SNOMED CT concept for the relationship
- relationshipGroup
integer: group ID for relationships that are grouped
- characteristicTypeId
integer64: 900000000000011006 = Inferred relationship
- modifierId
integer64: 900000000000451002 = Existential restriction modifier
- effectiveTime
IDate: when the concept became active
- typeId
integer64: type of relationship, e.g. 116680003 = Is a, 42752001 = Due to, 246090004 = Associated finding, 363698007 = Finding site, 363702006 = Has focus
See Also
Other SNOMEDsample:
HISTORY,
QUERY
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_SIMPLEMAP,
sampleSNOMED()
Examples
# Create a TEST environment and load the sample dictionaries
TEST <- new.env()
data(CONCEPT, envir = TEST)
data(DESCRIPTION, envir = TEST)
data(RELATIONSHIP, envir = TEST)
data(STATEDRELATIONSHIP, envir = TEST)
# Show properties of the relationship tables
str(TEST$RELATIONSHIP)
str(TEST$STATEDRELATIONSHIP)
Sample SIMPLE map table from SNOMED CT dictionary
Description
A sample of the SNOMED CT SIMPLE map table, containing maps to ICD-10 and OPCS4.
Usage
data(SIMPLEMAP)
Format
An object of class "data.table"
Details
- moduleId
integer64: core metadata concept: 900000000000207008 = SNOMED CT core module, 999000021000000109 = SNOMED CT United Kingdom clinical extension reference set module, 999000031000000106 = SNOMED CT United Kingdom Edition reference set module
- refsetId
integer64: foundation metadata concept: 900000000000497000 = CTV3 simple map reference set, 446608001 = ICD-O simple map reference set, 1323081000000108 = Coronavirus disease 19 caused by severe acute respiratory syndrome coronavirus 2 test result communication to general practice concept simple map reference set, 1323091000000105 = Coronavirus disease 19 caused by severe acute respiratory syndrome coronavirus 2 test result communication to general practice description simple map reference set, 82551000000107 = National Health Service England National Genomic Test Directory whole genome sequencing test simple map reference set
- referencedComponentId
integer64: SNOMED CT conceptId of the concept mapped
- mapTarget
character: target ICD-O or CTV3 code
- effectiveTime
IDate: when the concept became active
- active
logical: whether this concept is currently active
See Also
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
sampleSNOMED()
Examples
# Load the dataset and show its properties
data('SIMPLEMAP')
str(SIMPLEMAP)
# This SIMPLEMAP table is part of the sample SNOMED CT dictionary
# Hence this should show the same properties as above
str(sampleSNOMED()$SIMPLEMAP)
Convert a data.frame to a SNOMEDcodelist object
Description
SNOMEDcodelist is an S3 class for lists of SNOMED CT concepts. It consists of conceptId and include_desc columns. The option to include descendants allows the creation of more succinct SNOMED codelists.
Usage
SNOMEDcodelist(
x,
include_desc = FALSE,
format = c("simple", "tree", "exptree"),
codelist_name = NULL,
version = NULL,
author = NULL,
date = NULL,
SNOMED = getSNOMED(),
show_excluded_descendants = FALSE
)
as.SNOMEDcodelist(x, ...)
Arguments
x |
vector of SNOMED CT concept IDs, something which can be coerced to a SNOMEDconcept object, or a data.frame with a column 'conceptId' containing SNOMED CT concept concept IDs in integer64 or text format and optional column 'include_desc' (Boolean) stating whether descendants of the term should be included. |
include_desc |
Boolean vector stating whether descendants are included, recycled if necessary. Default = FALSE. Ignored if x contains a column 'include_desc' |
format |
Whether the codelist is expressed as a simple enumeration of concepts ('simple'), as a set of concept hierarchies ('tree'), or concept hierarchies showing all descendant terms ('exptree'). Codelists can be converted between the formats, but the result of conversion may depend on the SNOMED CT dictionary being used. |
codelist_name |
Name of the codelist (character vector of length 1) |
version |
Version of the codelist (character vector of length 1) |
author |
Author of the codelist (character vector of length 1) |
date |
Date attributed to the codelist (character vector of length 1) |
SNOMED |
environment containing a SNOMED dictionary |
show_excluded_descendants |
Whether to show excluded descendants alongside the codes included in the codelist (for a 'tree' or 'exptree' format codelist). |
... |
other arguments to pass to SNOMEDcodelist |
Details
Input is a data.frame or data.table with column names 'conceptId' and optionally 'include_desc', which is FALSE by default, but if TRUE then the codelist automatically includes all active descendants of that concept.
If the codelist is intended to contain inactive concepts, it can only exist in the 'simple' format. Inactive concepts will be lost if the codelist is converted between formats.
as.SNOMEDcodelist converts its argument into a SNOMEDcodelist but leaves it unchanged if it is already a SNOMEDcodelist.
Value
An object of class 'SNOMEDcodelist'
See Also
htmlCodelistHierarchy
Other SNOMEDcodelist functions:
addInactiveConcepts(),
expandSNOMED(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Other SNOMEDcodelist functions:
addInactiveConcepts(),
expandSNOMED(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Examples
SNOMED <- sampleSNOMED()
my_concepts <- SNOMEDconcept('Heart failure')
SNOMEDcodelist(my_concepts)
SNOMEDcodelist(data.frame(conceptId = my_concepts))
as.SNOMEDcodelist(data.frame(conceptId = my_concepts,
include_desc = TRUE))
Extract acronyms stated in the description of SNOMED CT concepts
Description
Returns acronyms, if any, expressed within SNOMED CT descriptions in the form 'ABCD - Another Bland Cardiovascular Disease'.
Usage
acronyms(conceptIds, SNOMED = getSNOMED())
Arguments
conceptIds |
character or integer64 vector |
SNOMED |
environment containing SNOMED dictionary. Defaults to an object named 'SNOMED' in the global environment |
Value
a data.table with the following columns: id, conceptId, type = 'Acronym', term = acronym
Examples
SNOMED <- sampleSNOMED()
acronyms('Heart failure')
# Get all synonyms and acronyms
rva <- SNOMEDconcept('Right ventricular abnormality')
rbind(description(rva, include_synonyms = TRUE), acronyms(rva))
Add composition lookups to CDB
Description
Creates a composition lookup table for a set of SNOMED CT concepts based on output of ‘decompose’, and adds it to the CDB
Usage
addComposeLookupToCDB(
decompositions,
CDB,
maxcol = 10,
SNOMED = getSNOMED(),
...
)
Arguments
decompositions |
vector of filenames of decompose output (read by fread) or data.frame containing outputs of decompose function |
CDB |
concept database environment, containing a table called FINDINGS |
maxcol |
maximum number of attributes columns. If NULL it is determined from the data. It might be helpful to specify it so that downstream databases and programs know exactly how many columns to expect. We suggest setting it to 10 which should handle all possible SNOMED CT concept decompositions. |
SNOMED |
environment containing a SNOMED CT dictionary |
... |
other arguments to pass to fread |
Value
CDB environment with an additional data.table ‘COMPOSELOOKUP’ with columns rootId, attr_1 ... attr_X (up to maxcol), with, due_to, without, origId (all with data type integer64 and class 'SNOMEDconcept')
See Also
decompose, compose, batchDecompose
Other CDB functions:
addWordnet(),
batchDecompose(),
createCDB(),
downloadOrphanet(),
downloadWordnet()
Add inactive concepts to a SNOMEDcodelist or SNOMEDconcept vector
Description
Adds SNOMED concepts linked by the QUERY or HISTORY tables that are mapped to or descendants of concepts in a SNOMEDcodelist or a SNOMEDconcept vector. If a SNOMEDcodelist, it is automatically converted to the 'simple' format (all items enumerated).
Usage
addInactiveConcepts(x, provenance = 0:3, SNOMED = getSNOMED())
Arguments
x |
SNOMEDcodelist or SNOMEDconcept object |
provenance |
vector of provenance values to use |
SNOMED |
SNOMED environment containing HISTORY and QUERY tables |
Details
It is recommended to use this function to convert a reference into a codelist for running a query against an electronic health record database which might contain historic SNOMED CT concepts.
Value
SNOMEDcodelist or SNOMEDconcept with linked inactive concepts included
See Also
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
expandSNOMED(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Use WordNet to assist concept database creation
Description
Adds terms from a WordNet thesaurus to a concept database, matching on term. It is recommended to restrict the wordnet categories to ensure that words with multiple meanings are not linked to the wrong synonym. This function also corrects some known errors in WordNet to avoid them being passed on to the CDB; currently this applies to 'allergy = allergic reaction', 'cuneiform bone = triquetral' and 'trauma' = 'injury', but more corrections can be done if needed.
Usage
addWordnet(
CDB_TABLE,
wn_categories,
WN,
CHECK_TABLE = NULL,
errors_to_remove = list(c("allergy", "allergic reaction"), c("allergic",
"allergic reaction"), c("trauma", "injury"), c("traumatic", "injury"), c("skinny",
"skin"), c("cuneiform bone", "triquetral bone"), c("upset", "disorder"),
c("disorderliness", "disorder")),
noisy = TRUE
)
Arguments
CDB_TABLE |
data.frame or data.table with columns conceptId (integer64) and term (character, with space before and after) containing existing descriptions in the CDB |
wn_categories |
WordNet categories to use |
WN |
WordNet data.table as returned by downloadWordnet |
CHECK_TABLE |
other table in the same format as CDB_TABLE to check for WordNet synonyms that link to another unrelated concept, where this synonym will be excluded because of the risk of errors |
errors_to_remove |
list of character vectors of length two containing synonym pairs to be removed. The first entry of the pair will be removed from the WordNet file before it is used for adding to CDB |
noisy |
whether to output test comments |
Value
CDB_TABLE with extra rows for Wordnet synonyms
References
https://wordnet.princeton.edu/
See Also
downloadWordnet
Other CDB functions:
addComposeLookupToCDB(),
batchDecompose(),
createCDB(),
downloadOrphanet(),
downloadWordnet()
Examples
WORDNET <- data.table::data.table(cat = c('noun.body', 'noun.state'),
wordnetId = bit64::as.integer64('1', '2'),
synonyms = list(c('heart', 'pump', 'ticker'),
c('infection', 'infectious')),
parents = list('cardiovascular system',
'pathologic process'),
adj = list('cardiac', 'infectious'))
# Add Wordnet synonyms to a concept database table
SNOMED <- sampleSNOMED()
CDB_TABLE <- description(c('Heart', 'Infection'),
include_synonyms = TRUE)[type == 'Synonym',
list(conceptId, term = paste0(' ', tolower(term), ' '))]
addWordnet(CDB_TABLE, 'noun.state', WORDNET)
Return the SNOMED CT concept IDs for a set of terms
Description
Carries out an exact or regular expression match to return the concept ID for a set of search terms, or converts a character, integer or integer64 vector to a SNOMEDconcept object.
Usage
## S3 method for class 'SNOMEDconcept'
as.data.frame(x, ...)
## S3 method for class 'SNOMEDconcept'
as.integer64(x, ...)
SNOMEDconcept(
x,
active_only = TRUE,
exact_match = TRUE,
unique = TRUE,
SNOMED = getSNOMED()
)
as.SNOMEDconcept(x, ...)
Arguments
x |
character vector of terms to match, or character vector containing SNOMED CT concept IDs, or 64-bit integer vector containing SNOMED CT concept IDs |
... |
additional arguments to send to grepl if using regular expression matching |
active_only |
whether or not to include inactive concepts |
exact_match |
if TRUE, only an exact (case sensitive) match is performed. If FALSE, a regular expression match is performed. |
unique |
whether to include no more than one instance of each SNOMED CT concept |
SNOMED |
environment containing SNOMED dictionary. Defaults to an object named 'SNOMED' in the global environment |
Value
a SNOMEDconcept object (vector of 64-bit integers) containing unique SNOMED CT concept IDs
See Also
Other SNOMEDconcept functions:
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Examples
SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED()) -> hf
is.SNOMEDconcept(hf)
SNOMEDconcept('900000000000003001')
as.SNOMEDconcept('900000000000003001')
Retrieve all attributes of a set of SNOMED CT concepts
Description
Returns the portion of the SNOMED CT relationship tables containing relationships where the given concepts are either the source or the destination.
Usage
attrConcept(
conceptIds,
SNOMED = getSNOMED(),
tables = c("RELATIONSHIP", "STATEDRELATIONSHIP"),
active_only = TRUE
)
Arguments
conceptIds |
character or integer64 vector of SNOMED concept IDs |
SNOMED |
environment containing a SNOMED dictionary |
tables |
character vector of relationship tables to use |
active_only |
whether to return only active attributes |
Value
a data.table with the following columns: sourceId (concept ID of source for relationship), destinationId (concept ID of source for relationship), typeId (concept ID of relationship type), typeName (description of relationship type)
Examples
SNOMED <- sampleSNOMED()
attrConcept(as.SNOMEDconcept('Heart failure'))
Creates a set of lookups for SNOMED composition
Description
Creates composition lookup table for a set of SNOMED CT concepts and exports the results to a file
Usage
batchDecompose(conceptIds, CDB, output_filename, SNOMED = getSNOMED(), ...)
Arguments
conceptIds |
SNOMED CT concept IDs for creating decompositions |
CDB |
concept database environment, containing a table called FINDINGS |
output_filename |
filename of output file |
SNOMED |
environment containing a SNOMED dictionary |
... |
other arguments to pass through to decompose |
Value
TRUE if successful
See Also
decompose, compose, addComposeLookupToCDB
Other CDB functions:
addComposeLookupToCDB(),
addWordnet(),
createCDB(),
downloadOrphanet(),
downloadWordnet()
Concatenate vectors of SNOMED CT concepts
Description
SNOMEDconcept is an S3 class for vectors of SNOMED concept IDs as 64-bit integers. This function concatenates two or more SNOMEDconcept vectors.
Usage
## S3 method for class 'SNOMEDconcept'
c(...)
Arguments
... |
SNOMEDconcept vectors |
Value
concatenation of vectors
See Also
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Examples
hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
hf2 <- c(hf, hf)
Select more specific SNOMED CT concepts based on attributes
Description
Finds the most specific SNOMED CT concepts that matches the combination of a root concept and attributes. Based on a composeLookup table
Usage
compose(
conceptId,
CDB,
attributes_conceptIds = bit64::integer64(0),
due_to_conceptIds = bit64::integer64(0),
without_conceptIds = bit64::integer64(0),
with_conceptIds = bit64::integer64(0),
SNOMED = getSNOMED()
)
Arguments
conceptId |
SNOMED CT concept to refine |
CDB |
SNOMED CT concept database, as created by createCDB. An environment containing the following data tables: FINDINGS, QUAL, CAUSES, BODY, FINDINGS, OTHERSUB, OVERLAP, TRANSITIVE. Must also contain a COMPOSELOOKUP table created by addComposeLookupToCDB |
attributes_conceptIds |
SNOMED concept Ids of attributes of concept e.g. laterality, severity, acuteness |
due_to_conceptIds |
SNOMED concept Ids of cause |
without_conceptIds |
SNOMED concept Ids of conditions stated to be absent |
with_conceptIds |
SNOMED concept Ids of conditions also present |
SNOMED |
environment containing SNOMED CT tables |
Value
a refined SNOMED concept Id
See Also
decompose, batchDecompose, addComposeLookupToCDB
Examples
## Not run:
SNOMED <- sampleSNOMED()
miniCDB <- createCDB(SNOMED)
D <- decompose('Cor pulmonale', CDB = miniCDB)
print(D)
# --------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# --------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)
# Compile decompositions into a lookup table
miniCDB <- addComposeLookupToCDB(D, CDB = miniCDB))
compose(as.SNOMEDconcept('Right heart failure'),
due_to_conceptIds = as.SNOMEDconcept('Disorder of lung'),
CDB = miniCDB, composeLookup = CL)
# [1] "83291003 | Cor pulmonale (disorder)"
## End(Not run)
Creates an environment containing CDB files
Description
Extracts SNOMED CT concepts from appropriate places in the hierarchy to create a set of CDB files in an environment. Uses WordNet and manual synonyms if available.
Usage
createCDB(
SNOMED = getSNOMED(),
TRANSITIVE = NULL,
WN = NULL,
MANUAL_SYNONYMS = NULL,
noisy = TRUE,
stopwords = c("the", "of", "by", "with", "to", "into", "and", "or", "at", "as",
"and/or", "in")
)
Arguments
SNOMED |
environment containing a SNOMED dictionary |
TRANSITIVE |
transitive closure table, generated by createTransitive. It is regenerated if not provided. |
WN |
WordNet data.table as returned by downloadWordnet containing WordNet data from appropriate categories, in the format: cat (character), wordnetId (integer64), synonyms (list), parents (list), adj (list) |
MANUAL_SYNONYMS |
data.table with columns term1 and term2, containing additional exact synonyms or abbreviations |
noisy |
whether to output status messages |
stopwords |
vector of stopwords |
Value
environment containing the following data tables: FINDINGS, QUAL, CAUSES, BODY, OTHERCAUSE, OTHERSEARCH, OVERLAP, TRANSITIVE
See Also
exportMiADECDB, MANUAL_SYNONYMS
Other CDB functions:
addComposeLookupToCDB(),
addWordnet(),
batchDecompose(),
downloadOrphanet(),
downloadWordnet()
Examples
# Not run
# data(MANUAL_SYNONYMS)
# WN <- downloadWordnet()
# MANUAL_SYNONYMS <- rbind(MANUAL_SYNONYMS, downloadOrphanet())
# CDBNEW <- createCDB(WN = WN, MANUAL_SYNONYMS = MANUAL_SYNONYMS)
Create indices for tables in a SNOMED environment
Description
Creates relevant indices for fast searching of SNOMED CT tables
Usage
createSNOMEDindices(SNOMED)
Arguments
SNOMED |
environment containing data.table objects: CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP |
Value
The environment with indices added to each table for fast searching
See Also
CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP, loadSNOMED, sampleSNOMED
Create a transitive closure table for is-a relationships for faster ancestor / descendant lookups
Description
Returns a data.table containing ancestor / descendant relationships which can be used in ancestors and descendants functions
Usage
createTransitive(
conceptIds,
SNOMED = getSNOMED(),
tables = c("RELATIONSHIP", "STATEDRELATIONSHIP")
)
Arguments
conceptIds |
character or integer64 vector of SNOMED concept IDs for the subset of concepts to include in the transitive table. |
SNOMED |
environment containing a SNOMED dictionary |
tables |
vector of names of relationship table(s) to use; by default use both RELATIONSHIP and STATEDRELATIONSHIP |
See Also
[ancestors()] and [descendants()]
Examples
SNOMED <- sampleSNOMED()
TRANSITIVE <- createTransitive('Heart failure')
Decomposition of meaning of a finding or disorder SNOMED CT concept
Description
Decomposes a SNOMED CT term into separate components according to the SNOMED CT information model and text parsing. Each term may have a number of possible decompositions. Requires a CDB environment created by createCDB.
Usage
decompose(
conceptIds,
diagnosis_text = NULL,
CDB,
SNOMED = getSNOMED(),
noisy = FALSE,
omit_unmatched = TRUE
)
Arguments
conceptIds |
vector of SNOMED CT concepts to decompose |
diagnosis_text |
vector of SNOMED CT terms (or in theory any text that has the same meaning as the SNOMED CT concept). If NULL, decompositions are created for all SNOMED CT synonyms of the concepts. |
CDB |
an environment containing CDB files, as created by createCDB |
SNOMED |
an environment containing the SNOMED CT dictionary |
noisy |
whether to output messages (for debugging) |
omit_unmatched |
whether to omit rows in which some attributes could not be matched to SNOMED CT concepts |
Value
a SNOMEDfinding objects, which is a data.table with columns rootId (integer64), with (integer64), due_to (integer64), after (integer64), without (integer64), body_site (integer64), severity (integer64), stage (integer64), laterality (integer64), roottext (character), partId (integer64), other_conceptId (character, a string of SNOMED CT concept IDs separated by spaces), origId (integer64)
Examples
## Not run:
miniCDB <- createCDB(SNOMED = sampleSNOMED())
D <- decompose(as.SNOMEDconcept('Cor pulmonale',
SNOMED = sampleSNOMED()), CDB = miniCDB, SNOMED = sampleSNOMED())
## End(Not run)
# -------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# -------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)
Obtain descriptions for a set of SNOMED CT terms
Description
Returns the descriptions matching a set of concept IDs from a SNOMED dictionary
Usage
description(
conceptIds,
include_synonyms = FALSE,
active_only = TRUE,
SNOMED = getSNOMED()
)
Arguments
conceptIds |
character or integer64 vector |
include_synonyms |
whether to return all synonyms, or just the Fully Specified Name, ensuring just one row per concept (default) |
active_only |
whether to include only active descriptions |
SNOMED |
environment containing SNOMED dictionary. Defaults to an object named 'SNOMED' in the global environment |
Value
a data.table with the following columns: id, conceptId, type (only if include_synonyms = TRUE), term, active (only if active_only = FALSE)
See Also
htmlCodelistHierarchy
Examples
hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
description(hf, include_synonyms = FALSE, SNOMED = sampleSNOMED())
Download Orphanet to assist with adding synonyms for rare diseases
Description
Downloads the Orphanet nomenclature pack and converts it into a format to be appended to MANUAL_SYNONYMS and used in concept database creation.
Usage
downloadOrphanet(
orphanet_url =
"https://www.orphadata.com/data/nomenclature/packs/Orphanet_Nomenclature_Pack_EN.zip",
masterfile_name = NULL,
SNOMED = getSNOMED()
)
Arguments
orphanet_url |
URL or filepath to Orphanet zip file |
masterfile_name |
name of xls file containing Orphanet synonyms. If omitted, it is assumed to be the only file with 'MasterFile' in its name, and the program will search for it in the Orphanet zip file. |
SNOMED |
environment containing SNOMED CT dictionary |
Value
data.table containing Orphanet synonyms in the format
References
https://www.orphadata.com/pack-nomenclature/
See Also
downloadWordnet, MANUAL_SYNONYMS
Other CDB functions:
addComposeLookupToCDB(),
addWordnet(),
batchDecompose(),
createCDB(),
downloadWordnet()
Examples
# Not run
# ORPHANET <- downloadOrphanet()
Download WordNet to assist concept database creation
Description
Downloads the WordNet thesaurus and converts it into a format to be used by addWordnet to add extra synonyms to a concept database.
Usage
downloadWordnet(
wordnet_url = "https://wordnetcode.princeton.edu/wn3.1.dict.tar.gz",
wn_categories = c("noun.body", "noun.state", "noun.process", "noun.animal",
"noun.plant", "noun.phenomenon")
)
Arguments
wordnet_url |
URL or filepath to WordNet tar.gz file |
wn_categories |
WordNet categories from which to extract data |
Value
data.table containing WordNet data from appropriate categories, in the format: cat (character), wordnetId (integer64), synonyms (list), parents (list), adj (list)
References
https://wordnet.princeton.edu/
See Also
addWordnet
Other CDB functions:
addComposeLookupToCDB(),
addWordnet(),
batchDecompose(),
createCDB(),
downloadOrphanet()
Examples
# Not run
# WORDNET <- downloadWordnet()
Sample inclusion, exclusion and blacklist sets for a MiADE CDB
Description
Returns a set of SNOMED concepts (as a SNOMEDconcept vector) which can be used to exclude findings in the MedCAT named entity recognition step, or blacklist (filter out) findings from the final output.
Usage
exclude_irrelevant_findings(SNOMED = getSNOMED())
blacklist_vague_findings(SNOMED = getSNOMED())
blacklist_almost_all_except_diseases(SNOMED = getSNOMED())
Arguments
SNOMED |
environment containing a SNOMED dictionary |
Details
- exclude_irrelevant_findings
social history (except housing problems and care needs), administrative statuses (except registered disabled) and for concept detection
- blacklist_vague_findings
vague findings and disorders, intended to be used in the blacklist
- blacklist_almost_all_except_diseases
almost all findings and vague disorders, intended to be used in the blacklist
Value
SNOMEDconcept vector containing findings to exclude
See Also
exportMiADECDB, createCDB
Other MiADE functions:
exportMiADECDB()
Other MiADE functions:
exportMiADECDB()
Other MiADE functions:
exportMiADECDB()
Expand or contract a SNOMEDcodelist
Description
SNOMEDcodelist is an S3 class for sets of SNOMED concepts. In the 'contracted' form, it may contain only parents and not child terms (to create a more succinct list). The 'Expanded' form contains all concepts. The output of 'showCodelistHierarchy' includes all hierarchies contained within the codelist in a format suitable for display.
Usage
expandSNOMED(x, SNOMED = getSNOMED(), ...)
contractSNOMED(x, SNOMED = getSNOMED(), ...)
showCodelistHierarchy(
x,
SNOMED = getSNOMED(),
max_excluded_descendants = 200,
...
)
Arguments
x |
SNOMEDcodelist to expand or contract. If x is not a SNOMEDcodelist, it is coerced to one by as.SNOMEDcodelist |
SNOMED |
environment containing a SNOMED dictionary |
... |
other arguments to pass to as.SNOMEDcodelist |
max_excluded_descendants |
(integer) whether to show excluded descendants as long as they do not exceed this number (a limit is suggested to avoid the program crashing if there are too many descendants). If this number is exceeded, the program will initially try to include children only, and if there are still too many, it will ignore all descendants. An 'included' column is added to the codelist showing which terms are included. This can make it easy to see if a codelist is consistent with the SNOMED CT ontology. |
Value
An object of class 'SNOMEDcodelist' with attribute Expanded = TRUE
See Also
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
export(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Examples
SNOMED <- sampleSNOMED()
my_concepts <- SNOMEDconcept('Heart failure')
my_codelist <- SNOMEDcodelist(data.frame(conceptId = my_concepts,
include_desc = TRUE))
expanded_codelist <- expandSNOMED(my_codelist)
contractSNOMED(expanded_codelist)
Export a SNOMEDcodelist
Description
Writes a SNOMEDcodelist to file. If the filename is NULL, a filename is created from the 'codelist_name' attribute.
Usage
export(x, ...)
## S3 method for class 'SNOMEDcodelist'
export(x, filename = NULL, metadata = TRUE, ...)
Arguments
x |
SNOMEDcodelist object to export to file |
... |
not used |
filename |
character vector of length 1 for the file to write to. If NULL, a filename is generated from the codelist filename. |
metadata |
whether metadata is exported (default = TRUE) |
Value
invisibly returns the exported codelist
See Also
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
expandSNOMED(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
expandSNOMED(),
is.SNOMEDcodelist(),
print.SNOMEDcodelist()
Exports CDB files for MedCAT / MiADE
Description
Produces a set of files for the findings / problems algorithm of MedCAT and MiADE. Uses the CDB environment created using createCDB which can incorporate additional manual synonyms or synonyms from WordNet.
Usage
exportMiADECDB(
CDB,
export_folderpath,
lang_refset_files = NULL,
exclude = NULL,
include = NULL,
exclude_historic = descendants("Disorder", SNOMED = getSNOMED()),
blacklist = NULL,
SNOMED = getSNOMED()
)
Arguments
CDB |
concept database environment created by createCDB |
export_folderpath |
folder path to export to |
lang_refset_files |
character vector of file paths to SNOMED CT language refset files, in order to identify the preferred term for each concept. If NULL, the Fully Specified Name minus the semantic type suffix is used as the preferred term (e.g. if the Fully Specified Name is ‘Cancer (disorder)’, the default preferred term is ‘Cancer’. |
exclude |
a SNOMEDconcept or SNOMEDcodelist object specifying concepts to exclude from the concept database. By default, all concepts in the FINDINGS, CAUSES, BODY, LATERALITY, MORPH, SEVERITY, STAGE and QUAL tables will be included. |
include |
a SNOMEDconcept or SNOMEDcodelist object specifying additional concepts to include in the concept database. By default, all findings are included for potential export, but there may additional concepts of other semantic types (e.g. situation concepts) that need to be included. Inclusion takes place after exclusion, i.e. a concept in both the include and exclude lists will be included. |
exclude_historic |
a SNOMEDconcept or SNOMEDcodelist object specifying concepts to be excluded from the ‘historic’ lookup, i.e. those that should not be converted into historic forms. The default is to not do this conversion for disorders, only for procedures. |
blacklist |
a SNOMEDconcept or SNOMEDcodelist object specifying concepts to filter out of the final output. By default, concepts in the CDB of any semantic type other than ‘finding’ or ‘disorder’ are excluded. The blacklist can be used to exclude a subset of findings or disorders that are not useful for the particular application. |
SNOMED |
environment containing a SNOMED dictionary |
Details
The following files are exported:
For MedCAT (named entity recognition and linking):
- problems_cdb.csv
- CSV file in MedCAT concept database format containing cui (SNOMED CT concept ID), name, name_status ('P' for preferred term, 'N' for terms that must be disambiguated (e.g. acronyms or short terms), 'A' for synonym), ontologies = SNO (for SNOMED CT)
For MiADE postprocessing:
- negated.csv
- CSV file with columns findingId (SNOMED CT concept ID of the underlying finding / disorder) and situationId (SNOMED CT concept ID of the pre-coordinated situation concept for negation of the finding / disorder). Sorted by findingId.
- historic.csv
- CSV file with columns findingId (SNOMED CT concept ID of the underlying finding / disorder) and situationId (SNOMED CT concept ID of the pre-coordinated situation concept for 'history of' the finding / disorder). Sorted by findingId.
- suspected.csv
- CSV file with columns findingId (SNOMED CT concept ID of the underlying finding / disorder) and situationId (SNOMED CT concept ID of the pre-coordinated situation concept for 'suspected' finding / disorder). Sorted by findingId.
- overlap.csv
- CSV file with columns findingId (SNOMED CT concept ID of the underlying finding / disorder) and otherId (SNOMED CT concept ID of a concept with the same description but a different semantic type, typically a morphologic abnormality). Sorted by otherId.
- problem_blacklist.csv
- CSV file without header with one column containing SNOMED CT concept IDs for concepts that may be identified by MedCAT as part of text analysis but should not be included in final MiADE output, Examples include procedure codes which may be used to link to precoordinated ‘history of...’ concepts. This file can also be used to force MiADE to ignore any specific SNOMED CT concepts in the output. Sorted in ascending order.
For more information about MiADE, visit https://www.ucl.ac.uk/health-informatics/research/miade/miade-software-and-availability
For more information about MedCAT, visit https://github.com/CogStack/MedCAT
Value
TRUE if successful
See Also
createCDB, downloadWordnet, downloadOrphanet, MANUAL_SYNONYMS, exclude_irrelevant_findings
Other MiADE functions:
exclude_irrelevant_findings()
Examples
# Not run
# exportMiADECDB(CDB, export_folderpath = tempdir())
Export a SNOMED environment to a folder
Description
Creates tab separated files which can be reloaded with relevant indices for fast searching of SNOMED CT tables
Usage
exportSNOMEDenvir(SNOMED, folder)
Arguments
SNOMED |
environment containing data.table objects: CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP |
folder |
path to folder where files will be written |
See Also
CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP
Obtain Read 2, CTV3, ICD-10 and OPCS4 maps for SNOMED CT concepts
Description
Returns concepts mapped to SNOMED CT from either the SIMPLEMAP table in the SNOMED dictionay (Clinical Terms Version 3, CTV3 maps, one per concept), the EXTENDEDMAP table (ICD-10 and OPCS4 maps) or a separate mapping table with Read Clinical Terms Version 2 (Read 2) and CTV3 maps. A sample mapping table (READMAPS) is provided.
Usage
getMaps(
x,
mappingtable = NULL,
to = c("read2", "ctv3", "icd10", "opcs4", "ctv3simple"),
SNOMED = getSNOMED(),
single_row_per_concept = TRUE
)
Arguments
x |
SNOMEDcodelist or SNOMEDconcept object. If it is a SNOMEDconcept object it is first converted to a SNOMEDcodelist. If it is a SNOMEDcodelist it is first converted to 'simple' format. Columns named 'read2_code' or 'read2_term' (if adding Read 2 maps) or 'ctv3_concept' or ctv3_termid' (if adding CTV3 maps) will be overwritten. |
mappingtable |
data.table containing mapping in the format described in 'Details'. The MAPS dataset in this package provides a sample. It must contain a unique field 'conceptId', and fields named 'read2_code' and 'read2_term' (for mapping to Read 2) or 'ctv3_concept' and 'ctv3_termid' (for mapping to CTV3). |
to |
character vector stating which terminologies to map to. Options are 'icd10', 'opcs4', 'ctv3simple' (use tables included within the SNOMED dictionary), or 'read2' or 'ctv3' (require a separate mapping table such as READMAPS). Beware that including multiple destination terminologies may result in a significant expansion of the number of rows if single_row_per_concept is FALSE. |
SNOMED |
an environment containing the SNOMED CT dictionary. If not supplied, it will be obtained using getSNOMED(). |
single_row_per_concept |
(logical) if TRUE (default), the function returns a single row per concept with Read 2 and CTV3 maps returned as lists (i.e. multiple entries within a single cell). This means the output is a valid SNOMEDcodelist object. If FALSE, returns multiple rows per concept (one for each map). |
Details
The mapping table can be created from the NHS Digital 'Data Migration' pack files which contain 'forward' maps of Read 2 and CTV3 to SNOMED CT. These are intended for converting individual entries in electronic health records to SNOMED CT. The 'forward' map files contain a SNOMED CT map for every Read 2 or CTV3 code, but not all the SNOMED CT concepts are mapped. Future SNOMED CT concepts will also not be mapped.
These maps can be used for converting SNOMED CT codelists into Read 2 or CTV3 format for running queries, such as to characterise patient phenotypes or identify patient populations for research. They cannot be used in the reverse direction (to map a Read 2/CTV3 codelist to SNOMED CT) because some of the SNOMED CT terms will be missed out, and the list will be incomplete.
The mapping table must be a data.table object with columns: conceptId (integer64, unique), read2_code (character list of 7-character Read 2 codes), read2_term (character list of Read 2 terms), ctv3_concept (character list of CTV3 concept codes), ctv3_termid (character list of CTV3 term description codes)
Value
a data.table containing the columns conceptId and either 'read2_code' and 'read2_term' (for mapping to Read 2), 'ctv3_concept' and 'ctv3_termid' (for mapping to CTV3 using the mapping table), 'ctv3_simple' (mapping to CTV3 using SIMPLEMAP within the SNOMED dictionary), 'icd10_code' or 'opcs4_code' (mapped using EXTENDEDMAP within the SNOMED dictionary). If single_row_per_concept is TRUE, the mapped rows are of type 'list' and the output is also a SNOMEDcodelist in 'simple' format, otherwise the output may have multiple rows per conceptId. Note that each Read 2, CTV3, ICD-10 or OPCS4 term may be mapped to multiple SNOMED CT concepts.
See Also
READMAPS, loadREADMAPS
Examples
# Load sample SNOMED CT dictionary into the global environment
# so it is available to the functions in this example
SNOMED <- sampleSNOMED()
# Use the sample READMAPS table in this package
data(READMAPS)
# Example: Mapping a single concept
getMaps(SNOMEDconcept('Heart failure'), mappingtable = READMAPS,
to = 'read2')
# Example: Mapping a concept and its descendants
getMaps(descendants(SNOMEDconcept('Heart failure')),
mappingtable = READMAPS, to = 'read2')
# Example: Mapping a codelist
getMaps(SNOMEDcodelist(SNOMEDconcept('Heart failure')),
mappingtable = READMAPS, to = c('ctv3', 'ctv3simple', 'icd10'))
Retrieves a Refset from the REFSET table
Description
Retrieves a Refset from the REFSET table
Usage
getRefset(conceptIds, SNOMED = getSNOMED())
Arguments
conceptIds |
character or integer64 vector of Refset SNOMED concept IDs, or something that can be coerced to a SNOMEDconcept |
SNOMED |
environment containing a SNOMED dictionary |
Value
a SNOMEDconcept vector of conceptIds of members of the selected refset(s)
Examples
SNOMED <- sampleSNOMED()
getRefset(c('Renal clinical finding simple reference set',
'Care planning activities simple reference set'))
Retrieves SNOMED CT dictionary from the global environment
Description
Returns an object named 'SNOMED' from the global environment. Returns an error if no such object exists, or if it is not an environment containing tables named CONCEPT, RELATIONSHIP, STATEDRELATIONSHIP and DESCRIPTION. There is no attempt to check that these tables are actually valid.
Usage
getSNOMED(SNOMEDname = "SNOMED")
Arguments
SNOMEDname |
name of the SNOMED environment to search for |
Value
SNOMED environment from the global environment
See Also
CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP, loadSNOMED, sampleSNOMED
Examples
SNOMED <- sampleSNOMED()
SNOMED2 <- getSNOMED()
# To display metadata for this SNOMED CT dictionary
SNOMED2$metadata
Whether SNOMED CT concepts have particular attributes
Description
For each concept in the first list, whether it has the attribute in the second list. Returns a vector of Booleans.
Usage
hasAttributes(
sourceIds,
destinationIds,
typeIds = bit64::as.integer64("116680003"),
SNOMED = getSNOMED(),
tables = c("RELATIONSHIP", "STATEDRELATIONSHIP"),
active_only = TRUE
)
Arguments
sourceIds |
character or integer64 vector of SNOMED concept IDs for children, recycled if necessary |
destinationIds |
character or integer64 vector of SNOMED concept IDs for parents, recycled if necessary |
typeIds |
character or integer64 vector of SNOMED concept IDs for renationship types, recycled if necessary. Defaults to 116680003 = 'Is a' (child/parent) |
SNOMED |
environment containing a SNOMED dictionary |
tables |
character vector of relationship tables to use |
active_only |
whether only active relationships should be considered, default TRUE |
Value
a vector of Booleans stating whether the attribute exists
Examples
data.table::setDTthreads(threads = 1) # for CRAN testing
SNOMED <- sampleSNOMED()
hasAttributes(c('Heart failure', 'Acute heart failure'),
c('Heart structure', 'Heart failure'),
c('Finding site', 'Is a'))
Export a SNOMEDcodelist hierarchy to HTML
Description
Exports a codelist with hierarchy as HTML for easy viewing.
Usage
htmlCodelistHierarchy(
x,
file = NULL,
title = NULL,
description = NULL,
extracols = NULL,
SNOMED = getSNOMED(),
...
)
Arguments
x |
a SNOMEDcodelist, codelistHierarchy (output of showCodelistHierarchy), or an object which can be coerced to a SNOMEDcodelist (such as a SNOMEDconcept vector). |
file |
filename to export to. If NULL, no file is written |
title |
title of HTML document |
description |
paragraph of description text (excluding <p></p> tags) |
extracols |
character vector of additional columns of codelist_with_hierarchy to include in HTML output |
SNOMED |
environment containing the SNOMED dictionary to use |
... |
extra arguments to pass to as.SNOMEDcodelist |
Value
a character vector containing HTML output
See Also
showCodelistHierarchy
Examples
data.table::setDTthreads(threads = 1) # for CRAN testing
SNOMED <- sampleSNOMED() # use sample SNOMED for testing
my_concepts <- SNOMEDconcept('Acute heart failure')
my_codelist <- SNOMEDcodelist(data.frame(conceptId = my_concepts,
include_desc = TRUE))
my_codelist <- getMaps(my_codelist, to = 'icd10')
htmlCodelistHierarchy(my_codelist, file = paste0(tempdir(),
'/codelist.html'), extracols = 'icd10_code')
# The codelist.html file can now be viewed in a web browser
# Clean up temporary file
file.remove(paste0(tempdir(), '/codelist.html'))
Check if inactive terms are included in SNOMED CT dictionary
Description
Checks the active_only flag in the metadata of a SNOMED environment to determine whether inactive terms are included
Usage
inactiveIncluded(SNOMED = getSNOMED())
Arguments
SNOMED |
environment containing SNOMED dictionary, defaults to an object named 'SNOMED' in the global environment |
Value
TRUE or FALSE (logical vector of length one)
Examples
# Create a TEST environment and load the sample dictionaries
TEST <- sampleSNOMED()
inactiveIncluded(TEST)
assign('metadata', list(active_only = TRUE), envir = TEST)
inactiveIncluded(TEST)
Check if an object is a SNOMEDcodelist
Description
SNOMEDcodelist is an S3 class for lists of SNOMED codes. This function checks whether the object has the class SNOMEDcodelist, and whether the specified attributes are as per the arguments (if the arguments are left as NULL, as per default, they are not checked). The function does not check if the codelist contains valid data.
Usage
is.SNOMEDcodelist(
x,
format = NULL,
codelist_name = NULL,
version = NULL,
author = NULL,
date = NULL,
SNOMED = NULL
)
Arguments
x |
object to check |
format |
Whether the codelist is expressed as a simple enumeration of concepts ('simple'), as a set of concept hierarchies ('tree') or as a set of hierarchies showing all concepts ('exptree'). Codelists can be converted between the formats, but the result of conversion may depend on the SNOMED CT dictionary being used. |
codelist_name |
Name of the codelist (character vector of length 1) |
version |
Version of the codelist (character vector of length 1) |
author |
Author of the codelist (character vector of length 1) |
date |
Date assigned to the codelist (character vector of length 1) |
SNOMED |
Dummy argument to ensure that this function works with as.SNOMEDcodelist |
Value
a logical vector of length one: TRUE or FALSE
See Also
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
expandSNOMED(),
export(),
print.SNOMEDcodelist()
Check if an object is a SNOMEDconcept
Description
SNOMEDconcept is an S3 class for vectors of SNOMED concept IDs as 64-bit integers. This function checks whether the object has the class SNOMEDconcept and is a vector of 64-bit integers.
Usage
is.SNOMEDconcept(x)
Arguments
x |
object to check |
Value
a logical vector of length one: TRUE or FALSE
See Also
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Load mappings from Read to SNOMED CT into an R data.table
Description
Creates a mapping table derived from NHS Digital Data Migration distribution. These tables are available from the Technology Reference Update Distribution: https://isd.digital.nhs.uk/trud/user/guest/group/0/pack/9/subpack/9/releases
Usage
loadREADMAPS(
not_assured_rcsctmap_uk,
not_assured_rctermsctmap_uk,
assured_ctv3sctmap2_uk
)
Arguments
not_assured_rcsctmap_uk |
File containing Read 2 codes mapped to SNOMED CT, in file: 'Not Clinically Assured/rcsctmap_uk_20200401000001.txt' |
not_assured_rctermsctmap_uk |
File containing Read 2 terms mapped to SNOMED CT, in file: 'Not Clinically Assured/rctermsctmap_uk_20200401000001.txt' |
assured_ctv3sctmap2_uk |
File containing CTV3 concepts and terms mapped to SNOMED CT, in file: 'Clinically Assured/ctv3sctmap2_uk_20200401000001.txt' |
Details
The final release was in April 2020. The mapping tables are intended for converting entries in clinical records from Read Version 2 (Read 2) to SNOMED CT, and Clinical Terms Version 3 (CTV3) to SNOMED CT.
These maps can be used for converting SNOMED CT codelists into Read 2 or CTV3 format for running queries, such as to characterise patient phenotypes or identify patient populations for research. They cannot be used in the reverse direction (to map a Read 2/CTV3 codelist to SNOMED CT) because some of the SNOMED CT terms will be missed out, and the list will be incomplete.
This function uses the following three mapping files:
- not_assured_rcsctmap_uk
File containing Read 2 codes mapped to SNOMED CT, in file: 'Not Clinically Assured/rcsctmap_uk_20200401000001.txt'
- not_assured_rctermsctmap_uk
File containing Read 2 terms mapped to SNOMED CT, in file: 'Not Clinically Assured/rctermsctmap_uk_20200401000001.txt'
- assured_ctv3sctmap2_uk
File containing CTV3 concepts and terms mapped to SNOMED CT, in file: 'Clinically Assured/ctv3sctmap2_uk_20200401000001.txt'
The output data.table has the following columns:
- conceptId
integer64: SNOMED CT conceptId (primary key)
- read2_code
list: character list of 7-character Read 2 codes
- read2_term
list: character list of Read 2 terms
- ctv3_concept
list: character list of CTV3 concept codes
- ctv3_termid
list: character list of CTV3 term description codes
Value
A data.table with columns conceptId, read2_code, ctv3_concept, ctv3_termid
See Also
READMAPS, getMaps, loadSNOMED
Load SNOMED CT files from a folder(s) into R data.table objects
Description
Identifies relevant SNOMED CT files from the folder structure of a SNOMED CT distribution. This includes the core 'Snapshot' tables mapping tables from the 'Refset' folder and the history substitution table and query table. The relevant tables are loaded into an R environment, which can be saved and then easily retrieved for future use. Files from two folders (e.g. International and UK versions) can be loaded together, and are automatically appended by the function.
Usage
loadSNOMED(folders, active_only = TRUE, version = NULL)
Arguments
folders |
Vector of folder paths containing SNOMED CT files |
active_only |
Whether to limit to current (active) SNOMED CT concepts |
version |
Version description. If NULL, it is derived from the folder paths and expressed in the form: INT date & UK date |
Details
The SNOMED CT files are available from the NHS Digital Technology Reference Update Distribution: https://isd.digital.nhs.uk/trud/user/guest/group/0/home
Value
An environment containing data.table objects: CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP, HISTORY (optional), QUERY (optional)
See Also
loadREADMAPS, CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP, QUERY, HISTORY sampleSNOMED, getSNOMED, exportSNOMEDenvir
Examples
# Create a TEST environment and load the sample dictionaries
TEST <- sampleSNOMED()
# Export to temporary directory
exportSNOMEDenvir(TEST, tempdir())
# Try to import using the loadSNOMED function
TEST2 <- loadSNOMED(tempdir(), active_only = FALSE)
# Check that reimported SNOMED dictionary is the same as the original
all.equal(TEST$CONCEPT, TEST2$CONCEPT)
all.equal(TEST$DESCRIPTION, TEST2$DESCRIPTION)
all.equal(TEST$RELATIONSHIP, TEST2$RELATIONSHIP)
all.equal(TEST$STATEDRELATIONSHIP, TEST2$STATEDRELATIONSHIP)
all.equal(TEST$REFSET, TEST2$REFSET)
all.equal(TEST$SIMPLEMAP, TEST2$SIMPLEMAP)
all.equal(TEST$EXTENDEDMAP, TEST2$EXTENDEDMAP)
Ancestors and descendants of SNOMED CT concepts
Description
Returns concepts with 'Is a' or inverse 'Is a' relationship with a set of target concepts. Ancestors include parents and all higher relations. Descendants include children and all lower relations.
Usage
parents(
conceptIds,
include_self = FALSE,
SNOMED = getSNOMED(),
TRANSITIVE = NULL,
...
)
ancestors(
conceptIds,
include_self = FALSE,
SNOMED = getSNOMED(),
TRANSITIVE = NULL,
...
)
children(
conceptIds,
include_self = FALSE,
SNOMED = getSNOMED(),
TRANSITIVE = NULL,
...
)
descendants(
conceptIds,
include_self = FALSE,
SNOMED = getSNOMED(),
TRANSITIVE = NULL,
...
)
Arguments
conceptIds |
character or integer64 vector of SNOMED concept IDs |
include_self |
whether to include the original concept(s) in the output, default = FALSE |
SNOMED |
environment containing a SNOMED dictionary |
TRANSITIVE |
transitive closure table for ancestors and descendants, containing is-a relationships. This table can be created by createTransitive to speed up the ancestor / descendant functions. If a TRANSITIVE table is provided, the SNOMED environment is not used and relatedConcepts is not called. TRANSITIVE should be a data.table with columns ancestorId and descendantId. |
... |
other arguments to pass to relatedConcepts |
Value
a bit64 vector of SNOMED CT concepts
See Also
[createTransitive()] for creation of TRANSITIVE table, and [relatedConcepts()] for the underlying function to extract SNOMED CT relationships.
Examples
SNOMED <- sampleSNOMED()
parents('Heart failure')
children('Heart failure')
ancestors('Heart failure')
descendants('Heart failure')
Display a SNOMEDcodelist on screen
Description
Displays a SNOMEDcodelist on screen, including metadata. Truncates term descriptions in order to fit within the line width.
Usage
## S3 method for class 'SNOMEDcodelist'
print(x, ...)
Arguments
x |
SNOMEDcodelist object to print to screen |
... |
not used |
Value
invisibly returns the codelist
See Also
Other SNOMEDcodelist functions:
SNOMEDcodelist(),
addInactiveConcepts(),
expandSNOMED(),
export(),
is.SNOMEDcodelist()
Display a SNOMEDconcept object with descriptions
Description
SNOMEDconcept is an S3 class for vectors of SNOMED concept IDs as 64-bit integers. This function checks whether the object has the class SNOMEDconcept and is a vector of 64-bit integers.
Usage
## S3 method for class 'SNOMEDconcept'
print(x, ...)
Arguments
x |
SNOMEDconcept object, or something that can be coerced to one |
... |
not required |
Value
invisibly returns a character vector of the SNOMED CT concepts with descriptions separated by pipe (|)
See Also
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
union.SNOMEDconcept(),
unique.SNOMEDconcept()
Print method for output of 'decompose' function
Description
Print method for output of 'decompose' function
Usage
## S3 method for class 'SNOMEDfindings'
print(x, ...)
Arguments
x |
SNOMEDfindings object to display |
... |
not used |
Value
TRUE
Obtain related concepts for a set of SNOMED CT concepts
Description
Returns concepts with a particular relation to a supplied set of SNOMED CT concepts
Usage
relatedConcepts(
conceptIds,
typeId = bit64::as.integer64("116680003"),
tables = c("RELATIONSHIP", "STATEDRELATIONSHIP"),
reverse = FALSE,
recursive = FALSE,
active_only = TRUE,
SNOMED = getSNOMED()
)
Arguments
conceptIds |
character or integer64 vector |
typeId |
concept ID of relationship type. Defaults to 116680003 = Is a |
tables |
vector of names of relationship table(s) to use; by default use both RELATIONSHIP and STATEDRELATIONSHIP |
reverse |
whether to reverse the relationship |
recursive |
whether to re-apply the function on the outputs |
active_only |
whether to limit the output to active concepts only |
SNOMED |
environment containing a SNOMED dictionary |
Value
a data.table with the following columns: id, conceptId, type (only if include_synonyms = TRUE), term, active (only if active_only = FALSE)
Examples
# Load sample SNOMED CT dictionary
SNOMED <- sampleSNOMED()
# Example: anatomical site of a finding
findingSite <- function(x){
relatedConcepts(as.SNOMEDconcept(x),
typeId = as.SNOMEDconcept('Finding site'))
}
description(findingSite('Heart failure'))
# Heart structure (body structure)
Sample SNOMED CT dictionary
Description
Returns an environment containing a selection of SNOMED CT terms, their relationships and descriptions which are provided with the package
Usage
sampleSNOMED()
Value
environment containing four data.table objects: CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP and a list named 'metadata'
See Also
CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP, REFSET, SIMPLEMAP, EXTENDEDMAP, HISTORY, QUERY, loadSNOMED, sampleSNOMED
Other sampleSNOMED:
HISTORY,
READMAPS,
SNOMED_CONCEPT,
SNOMED_DESCRIPTION,
SNOMED_EXTENDEDMAP,
SNOMED_REFSET,
SNOMED_RELATIONSHIP,
SNOMED_SIMPLEMAP
Examples
TEST <- sampleSNOMED()
inactiveIncluded(TEST)
SNOMEDconcept('Heart failure', SNOMED = TEST)
# To display metadata for this SNOMED CT dictionary
sampleSNOMED()$metadata
Retrieves semantic types using the text 'tag' in the description
Description
Uses the fully specified name in the DESCRIPTION table. If there are multiple fully specified names, the name with the most recent effectiveTime will be used.
Usage
semanticType(conceptIds, SNOMED = getSNOMED())
Arguments
conceptIds |
character or integer64 vector of SNOMED concept IDs |
SNOMED |
environment containing a SNOMED dictionary |
Value
a character vector of semantic tags corresponding to the conceptIDs
Examples
SNOMED <- sampleSNOMED()
semanticType(as.SNOMEDconcept(c('Heart failure', 'Is a')))
Retrieves closest single ancestor within a given set of SNOMED CT concepts
Description
Returns a vector of SNOMED CT concept IDs for an ancestor of each concept that is within a second list. If multiple ancestors are included in the second list, the concept is not simplified (i.e. the original concept ID is returned). This functionality can be used to translate concepts into simpler forms for display, e.g. 'Heart failure' instead of 'Heart failure with reduced ejection fraction'.
Usage
simplify(
conceptIds,
ancestorIds,
SNOMED = getSNOMED(),
tables = c("RELATIONSHIP", "STATEDRELATIONSHIP")
)
Arguments
conceptIds |
character or integer64 vector of SNOMED concept IDs for concepts for which an ancestor is sought |
ancestorIds |
character or integer64 vector of SNOMED concept IDs for possible ancestors |
SNOMED |
environment containing a SNOMED dictionary |
tables |
character vector of relationship tables to use |
Details
This function is intended for use with active SNOMED CT concepts only.
Value
a data.table with the following columns: originalId (integer64) = original conceptId, ancestorId (integer64) = closest single ancestor, or original concept ID if no ancestor is included among ancestorIds
Examples
SNOMED <- sampleSNOMED()
original_terms <- c('Systolic heart failure', 'Is a',
'Heart failure with reduced ejection fraction',
'Acute kidney injury due to circulatory failure (disorder)')
# Note in this example 'Is a' has no parents in ancestors,
# and acute kidney failure has two parents in ancestors
# so neither of the parents will be chosen.
# Also test out inclusion of duplicate concepts.
ancestors <- simplify(c(as.SNOMEDconcept(original_terms),
as.SNOMEDconcept(original_terms)[3:4]),
as.SNOMEDconcept(c('Heart failure', 'Acute heart failure',
'Cardiorenal syndrome (disorder)')))
print(cbind(original_terms, description(ancestors$ancestorId)$term))
Set operations for SNOMEDconcept vectors
Description
The default set functions in the base package do not handle integer64 vectors correctly, so this package also provides new generic functions for union, intersect and setdiff, which enable the appropriate object-specific function to be called according to the class of the vector. This means that SNOMEDconcept vectors will remain as SNOMEDconcept vectors when these functions are used.
Usage
## S3 method for class 'SNOMEDconcept'
union(x, y)
union(x, y)
## Default S3 method:
union(x, y)
## S3 method for class 'SNOMEDconcept'
intersect(x, y)
intersect(x, y)
## Default S3 method:
intersect(x, y)
## S3 method for class 'SNOMEDconcept'
setdiff(x, y)
setdiff(x, y)
## Default S3 method:
setdiff(x, y)
Arguments
x |
SNOMEDconcept vector |
y |
SNOMEDconcept vector, or an object that can be coerced to SNOMEDconcept by as.SNOMEDconcept |
Value
an integer64 vector of SNOMEDconcept class
See Also
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
unique.SNOMEDconcept()
Examples
sys_acute <- SNOMEDconcept(c('Systolic heart failure',
'Acute heart failure'), SNOMED = sampleSNOMED())
acute_left_right <- SNOMEDconcept(c('Acute heart failure',
'Left heart failure', 'Right heart failure'),
SNOMED = sampleSNOMED())
union(sys_acute, acute_left_right)
intersect(sys_acute, acute_left_right)
setdiff(sys_acute, acute_left_right)
Unique vector of SNOMED CT concepts
Description
SNOMEDconcept is an S3 class for vectors of SNOMED concept IDs as 64-bit integers. This function returns a vector containing only unique SNOMEDconcept values.
Usage
## S3 method for class 'SNOMEDconcept'
unique(x, ...)
Arguments
x |
SNOMEDconcept vector |
... |
other variables to pass on to the underlying 'unique' function |
Value
SNOMEDconcept vector with duplicates removed
See Also
Other SNOMEDconcept functions:
as.data.frame.SNOMEDconcept(),
c.SNOMEDconcept(),
is.SNOMEDconcept(),
print.SNOMEDconcept(),
union.SNOMEDconcept()
Examples
hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
hf2 <- c(hf, hf)
unique(hf2)