Package 'Rdiagnosislist' reference manual

Title:	Manipulate SNOMED CT Diagnosis Lists
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. For more information about 'SNOMED CT' visit <https://www.snomed.org/>.
Authors:	Anoop Shah [aut, cre]
Maintainer:	Anoop Shah <[email protected]>
License:	GPL-3
Version:	1.3
Built:	2024-11-28 06:54:06 UTC
Source:	CRAN

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())
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))
SNOMED <- sampleSNOMED()
acronyms('Heart failure')

# Get all synonyms and acronyms
rva <- SNOMEDconcept('Right ventricular abnormality')
rbind(description(rva, include_synonyms = TRUE), acronyms(rva))

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())
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

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' and 'cuneiform bone = triquetral' 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("cuneiform bone", "triquetral bone"), c("upset", "disorder"),
    c("disorderliness", "disorder"))
)
addWordnet(
  CDB_TABLE,
  wn_categories,
  WN,
  CHECK_TABLE = NULL,
  errors_to_remove = list(c("allergy", "allergic reaction"), c("allergic",
    "allergic reaction"), c("cuneiform bone", "triquetral bone"), c("upset", "disorder"),
    c("disorderliness", "disorder"))
)

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

Value

CDB_TABLE with extra rows for Wordnet synonyms

References

https://wordnet.princeton.edu/

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)
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)

Returns 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, ...)
## 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

Examples

SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED()) -> hf
is.SNOMEDconcept(hf)
SNOMEDconcept('900000000000003001')
as.SNOMEDconcept('900000000000003001')
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
)
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'))
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

Usage

batchDecompose(conceptIds, CDB, output_filename, SNOMED = getSNOMED(), ...)
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
`...`	out

Value

TRUE if successful

Examples

# Load the SNOMED dictionary (for this example we are using the
# sample included with the package)
SNOMED <- sampleSNOMED()
# Create a concept database environment
miniCDB <- createCDB(SNOMED = SNOMED)
# Create a decomposition
D <- decompose('Cor pulmonale', CDB = miniCDB, noisy = TRUE)
print(D)
# Load the SNOMED dictionary (for this example we are using the
# sample included with the package)
SNOMED <- sampleSNOMED()
# Create a concept database environment
miniCDB <- createCDB(SNOMED = SNOMED)
# Create a decomposition
D <- decompose('Cor pulmonale', CDB = miniCDB, noisy = TRUE)
print(D)

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(...)
## S3 method for class 'SNOMEDconcept'
c(...)

Arguments

...

SNOMEDconcept vectors

Value

concatenation of vectors

Examples

hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
hf2 <- c(hf, hf)
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,
  composeLookup,
  attributes_conceptIds = bit64::integer64(0),
  due_to_conceptIds = bit64::integer64(0),
  without_conceptIds = bit64::integer64(0),
  with_conceptIds = bit64::integer64(0),
  SNOMED = getSNOMED(),
  show_all_matches = FALSE
)
compose(
  conceptId,
  CDB,
  composeLookup,
  attributes_conceptIds = bit64::integer64(0),
  due_to_conceptIds = bit64::integer64(0),
  without_conceptIds = bit64::integer64(0),
  with_conceptIds = bit64::integer64(0),
  SNOMED = getSNOMED(),
  show_all_matches = FALSE
)

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
`composeLookup`	lookup table created by createComposeLookup
`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
`show_all_matches`	whether to stop if an exact match is found, or continue to search for all potential matches

Value

a refined SNOMED concept Id

Examples

SNOMED <- sampleSNOMED()
miniCDB <- createCDB(SNOMED)

D <- decompose('Cor pulmonale', CDB = miniCDB)
print(D)

# --------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# --------------------------------------------------------
# Root : 128404006 | Right heart failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)
#
# --------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# --------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)

# Compile decompositions into a lookup table
CL <- createComposeLookup(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)"
SNOMED <- sampleSNOMED()
miniCDB <- createCDB(SNOMED)

D <- decompose('Cor pulmonale', CDB = miniCDB)
print(D)

# --------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# --------------------------------------------------------
# Root : 128404006 | Right heart failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)
#
# --------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# --------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 19829001 | Disorder of lung (disorder)

# Compile decompositions into a lookup table
CL <- createComposeLookup(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)"

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", "both", "at", "as",
    "and/or", "in")
)
createCDB(
  SNOMED = getSNOMED(),
  TRANSITIVE = NULL,
  WN = NULL,
  MANUAL_SYNONYMS = NULL,
  noisy = TRUE,
  stopwords = c("the", "of", "by", "with", "to", "into", "and", "or", "both", "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

Examples

# Not run
# data(MANUAL_SYNONYMS)
# WN <- downloadWordnet()
# MANUAL_SYNONYMS <- rbind(MANUAL_SYNONYMS, downloadOrphanet())
# CDBNEW <- createCDB(WN = WN, MANUAL_SYNONYMS = MANUAL_SYNONYMS)
# Not run
# data(MANUAL_SYNONYMS)
# WN <- downloadWordnet()
# MANUAL_SYNONYMS <- rbind(MANUAL_SYNONYMS, downloadOrphanet())
# CDBNEW <- createCDB(WN = WN, MANUAL_SYNONYMS = MANUAL_SYNONYMS)

Creates a set of lookups for SNOMED composition

Description

Creates composition lookup table for a set of SNOMED CT concepts

Usage

createComposeLookup(
  decompositions,
  CDB,
  maxcol = 10,
  SNOMED = getSNOMED(),
  ...
)
createComposeLookup(
  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

data.table

Examples

# Not run

# mylookup <- createComposeLookup(D)
# Not run

# mylookup <- createComposeLookup(D)

Create indices for tables in a SNOMED environment

Description

Creates relevant indices for fast searching of SNOMED CT tables

Usage

createSNOMEDindices(SNOMED)
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

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")
)
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

Examples

SNOMED <- sampleSNOMED()

TRANSITIVE <- createTransitive('Heart failure')
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
)
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

Examples

miniCDB <- createCDB(SNOMED = sampleSNOMED())
D <- decompose(as.SNOMEDconcept('Cor pulmonale',
  SNOMED = sampleSNOMED()), CDB = miniCDB, SNOMED = sampleSNOMED())

# -------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# -------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 70995007 | Pulmonary hypertension (disorder)
miniCDB <- createCDB(SNOMED = sampleSNOMED())
D <- decompose(as.SNOMEDconcept('Cor pulmonale',
  SNOMED = sampleSNOMED()), CDB = miniCDB, SNOMED = sampleSNOMED())

# -------------------------------------------------------
# 83291003 | Cor pulmonale (disorder)
# -------------------------------------------------------
# Root : 367363000 | Right ventricular failure (disorder)
# - Due to : 70995007 | Pulmonary hypertension (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()
)
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)

Examples

hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
description(hf, include_synonyms = FALSE, SNOMED = sampleSNOMED())
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()
)
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/

Examples

# Not run
# ORPHANET <- downloadOrphanet()
# 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")
)
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/

Examples

# Not run
# WORDNET <- downloadWordnet()
# 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())
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

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,
  ...
)
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

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)
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, ...)
export(x, ...)

## S3 method for class 'SNOMEDcodelist'
export(x, filename = NULL, ...)

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.

Value

invisibly returns the exported codelist

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()
)
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

Examples

# Not run
# exportMiADECDB(CDB, export_folderpath = tempdir())
# 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)
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

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
)
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.

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'))
# 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())
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'))
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")
getSNOMED(SNOMEDname = "SNOMED")

Arguments

SNOMEDname

name of the SNOMED environment to search for

Value

SNOMED environment from the global environment

Examples

SNOMED <- sampleSNOMED()
SNOMED2 <- getSNOMED()

# To display metadata for this SNOMED CT dictionary
SNOMED2$metadata
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
)
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

SNOMED <- sampleSNOMED()

hasAttributes(c('Heart failure', 'Acute heart failure'),
  c('Heart structure', 'Heart failure'),
  c('Finding site', 'Is a'))
SNOMED <- sampleSNOMED()

hasAttributes(c('Heart failure', 'Acute heart failure'),
  c('Heart structure', 'Heart failure'),
  c('Finding site', 'Is a'))

Sample history substitution table from SNOMED CT dictionary

Description

Sample of the SNOMED CT table showing current equivalents for inactive concepts.

Usage

data(HISTORY)
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

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)
# 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)

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(),
  ...
)
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

Examples

SNOMED <- sampleSNOMED()

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'))
SNOMED <- sampleSNOMED()

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())
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)
# 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
)
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

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)
is.SNOMEDconcept(x)

Arguments

`x`	object to check

Value

a logical vector of length one: TRUE or FALSE

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
)
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 entires in clinical records from Read Version 2 (Read 2) to SNOMED CT, and Clinical Terms Version 3 (CTV3) to SNOMED CT.

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

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)
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)

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)
# 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)

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)
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.

Examples

data(MANUAL_SYNONYMS)
str(MANUAL_SYNONYMS)
data(MANUAL_SYNONYMS)
str(MANUAL_SYNONYMS)

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,
  ...
)
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

Examples

SNOMED <- sampleSNOMED()

parents('Heart failure')
children('Heart failure')
ancestors('Heart failure')
descendants('Heart failure')
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, ...)
## S3 method for class 'SNOMEDcodelist'
print(x, ...)

Arguments

`x`	SNOMEDcodelist object to print to screen
`...`	not used

Value

invisibly returns the codelist

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, ...)
## 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 (|)

Print method for output of 'decompose' function

Description

Print method for output of 'decompose' function

Usage

## S3 method for class 'SNOMEDfindings'
print(x, ...)
## S3 method for class 'SNOMEDfindings'
print(x, ...)

Arguments

`x`	SNOMEDfindings object to display
`...`	not used

Value

TRUE

Sample query table from SNOMED CT dictionary

Description

Sample of the SNOMED CT table of ancestor / descendant relationships for inactive concepts.

Usage

data(QUERY)
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

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)
# 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)
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

Examples

# Show properties of the READMAPS table
data(READMAPS)
str(READMAPS)
# Show properties of the READMAPS table
data(READMAPS)
str(READMAPS)

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()
)
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)
# 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()
sampleSNOMED()

Value

environment containing four data.table objects: CONCEPT, DESCRIPTION, RELATIONSHIP, STATEDRELATIONSHIP and a list named 'metadata'

Examples

TEST <- sampleSNOMED()
inactiveIncluded(TEST)
SNOMEDconcept('Heart failure', SNOMED = TEST)

# To display metadata for this SNOMED CT dictionary
sampleSNOMED()$metadata
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())
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')))
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")
)
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))
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))

Sample concept table from SNOMED CT dictionary

Description

A sample of the SNOMED CT concept table.

Usage

data(CONCEPT)
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

Examples

# Show properties of the CONCEPT table
data('CONCEPT')
str(CONCEPT)
# 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)
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

Examples

# Show properties of the DESCRIPTION table
data('DESCRIPTION')
str(DESCRIPTION)
# 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)
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

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)
# 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 spave.

Usage

data(REFSET)
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

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)
# 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
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

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)
# 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)
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

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)
# 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, ...)
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'

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))
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))

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)
## 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

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)
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, ...)
## 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

Examples

hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
hf2 <- c(hf, hf)
unique(hf2)
hf <- SNOMEDconcept('Heart failure', SNOMED = sampleSNOMED())
hf2 <- c(hf, hf)
unique(hf2)

Package 'Rdiagnosislist'

Help Index

Extract acronyms stated in the description of SNOMED CT concepts

Description

Usage

Arguments

Value

Examples

Add inactive concepts to a SNOMEDcodelist or SNOMEDconcept vector

Description

Usage

Arguments

Details

Value

See Also

Use WordNet to assist concept database creation

Description

Usage

Arguments

Value

References

See Also

Examples

Returns the SNOMED CT concept IDs for a set of terms

Description

Usage

Arguments

Value

See Also

Examples

Retrieve all attributes of a set of SNOMED CT concepts

Description

Usage

Arguments

Value

Examples

Creates a set of lookups for SNOMED composition

Description

Usage

Arguments

Value

See Also

Examples

Concatenate vectors of SNOMED CT concepts

Description

Usage

Arguments

Value

See Also

Examples

Select more specific SNOMED CT concepts based on attributes

Description

Usage

Arguments

Value

See Also

Examples

Creates an environment containing CDB files

Description

Usage

Arguments

Value

Examples

Creates a set of lookups for SNOMED composition

Description

Usage

Arguments

Value

See Also

Examples

Create indices for tables in a SNOMED environment

Description

Usage

Arguments

Value

See Also

Create a transitive closure table for is-a relationships for faster ancestor / descendant lookups

Description

Usage

Arguments