Concept map equivalence types

Description

The following values are supported:

• RELATEDTO - The concepts are related to each other, and have at least some overlap in meaning, but the exact relationship is not known.

• EQUIVALENT - The definitions of the concepts mean the same thing (including when structural implications of meaning are considered) (i.e. extensionally identical).

• EQUAL - The definitions of the concepts are exactly the same (i.e. only grammatical differences) and structural implications of meaning are identical or irrelevant (i.e. intentionally identical).

• WIDER - The target mapping is wider in meaning than the source concept.

• SUBSUMES - The target mapping subsumes the meaning of the source concept (e.g. the source is-a target).

• NARROWER - The target mapping is narrower in meaning than the source concept. The sense in which the mapping is narrower SHALL be described in the comments in this case, and applications should be careful when attempting to use these mappings operationally.

• SPECIALIZES - The target mapping specializes the meaning of the source concept (e.g. the target is-a source).

• INEXACT - There is some similarity between the concepts, but the exact relationship is not known.

• UNMATCHED - This is an explicit assertion that there is no mapping between the source and target concept.

• DISJOINT - This is an explicit assertion that the target concept is not in any way related to the source concept.

Usage

Equivalence

Format

An object of class list of length 10.

See Also

FHIR R4 - ConceptMapEquivalence

LOINC system URI

Description

The URI of the LOINC code system: http://loinc.org.

Usage

LOINC_URI

Format

An object of class character of length 1.

See Also

Using LOINC with HL7 Standards

FHIR MIME types

Description

The following MIME types are supported:

• FHIR_JSON: FHIR resources encoded as JSON

• FHIR_XML: FHIR resources encoded as XML

Usage

MimeType

Format

An object of class list of length 2.

See Also

FHIR R4 - Resource Formats

Coding property data types

Description

The following data types are supported:

• STRING - A string value.

• INTEGER - An integer value.

• BOOLEAN - A boolean value.

• DECIMAL - A decimal value.

• DATETIME - A date/time value.

• CODE - A code value.

• CODING - A Coding value.

Usage

PropertyType

Format

An object of class list of length 7.

See Also

FHIR R4 - Data Types

SNOMED CT system URI

Description

The URI of the SNOMED CT code system: http://snomed.info/sct.

Usage

SNOMED_URI

Format

An object of class character of length 1.

See Also

Using SNOMED CT with HL7 Standards

SaveMode

Description

The following save modes are supported:

• OVERWRITE: Overwrite any existing data.

• APPEND: Append the new data to the existing data.

• IGNORE: Only save the data if the file does not already exist.

• ERROR: Raise an error if the file already exists.

• MERGE: Merge the new data with the existing data based on resource ID.

Usage

SaveMode

Format

An object of class list of length 5.

Terminology cache storage type

Description

The type of storage to use for the terminology cache.

Usage

StorageType

Format

An object of class list of length 2.

Details

The following values are supported:

• MEMORY - Use an in-memory cache

• DISK - Use a disk-based cache

FHIR versions

Description

The following FHIR versions are supported:

• R4: FHIR R4

Usage

Version

Format

An object of class list of length 1.

Synthetic conditions data

Description

A synthetic data set of simplified and flattened FHIR Condition resources generated by Synthea.

Usage

conditions

Format

An object of class data.frame with 19 rows and 6 columns.

Details

A data frame with 19 rows and 6 columns:

• START - The onset date

• STOP - The abatement date

• PATIENT - The ID of the patient

• ENCOUNTER - The ID of the encounter

• CODE - The SNOMED CT code of the condition

• DESCRIPTION - The display name of the condition

Get data for a resource type from a data source

Description

Get data for a resource type from a data source

Usage

ds_read(ds, resource_code)

Arguments

Value

A Spark DataFrame containing the data for the given resource type.

Examples

## Not run: 
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()
data_source %>% ds_read('Condition') %>% sparklyr::sdf_nrow()

## End(Not run)

Execute a SQL on FHIR view

Description

Executes a SQL on FHIR view definition and returns the result as a Spark DataFrame.

Usage

ds_view(
  ds,
  resource,
  select = NULL,
  constants = NULL,
  where = NULL,
  json = NULL
)

Arguments

Value

A Spark DataFrame containing the results of the view.

See Also

Pathling documentation - SQL on FHIR

Examples

## Not run: 
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))
data_source %>% ds_view('Patient',
     select = list(
       list(
         column = list(
           list(path = 'id', name = 'id'),
           list(path = 'gender', name = 'gender'),
           list(path = "telecom.where(system='phone').value", 
                             name = 'phone_numbers', collection = TRUE)
         )
       ),
       list(
         forEach = 'name',
         column = list(
           list(path = 'use', name = 'name_use'),
           list(path = 'family', name = 'family_name')
         ),
         select = list(
           list(
             forEachOrNull = 'given',
             column = list(
               list(path = '$this', name = 'given_name')
             )
           )
         )
       )
     ),
     where = list(
        list(path = "gender = 'male'")
     )
)

## End(Not run)

Write FHIR data to Delta files

Description

Writes the data from a data source to a directory of Delta files.

Usage

ds_write_delta(ds, path, save_mode = SaveMode$OVERWRITE)

Arguments

Value

No return value, called for side effects only.

See Also

Pathling documentation - Writing Delta

SaveMode

Other data sink functions: ds_write_ndjson(), ds_write_parquet(), ds_write_tables()

Examples

pc <- pathling_connect()
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))

# Write the data to a directory of Delta files.
data_source %>% ds_write_delta(file.path(tempdir(), 'delta'), save_mode = SaveMode$OVERWRITE)

pathling_disconnect(pc)

Write FHIR data to NDJSON files

Description

Writes the data from a data source to a directory of NDJSON files. The files will be named using the resource type and the ".ndjson" extension.

Usage

ds_write_ndjson(ds, path, save_mode = SaveMode$ERROR, file_name_mapper = NULL)

Arguments

Value

No return value, called for side effects only.

See Also

Pathling documentation - Writing NDJSON

Other data sink functions: ds_write_delta(), ds_write_parquet(), ds_write_tables()

Examples

## Not run: 
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))

# Write the data to a directory of NDJSON files.
data_source %>% ds_write_ndjson(file.path(tempdir(), 'ndjson'))

## End(Not run)

Write FHIR data to Parquet files

Description

Writes the data from a data source to a directory of Parquet files.

Usage

ds_write_parquet(ds, path, save_mode = SaveMode$ERROR)

Arguments

Value

No return value, called for side effects only.

See Also

Pathling documentation - Writing Parquet

Other data sink functions: ds_write_delta(), ds_write_ndjson(), ds_write_tables()

Examples

pc <- pathling_connect()
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))

# Write the data to a directory of Parquet files.
data_source %>% ds_write_parquet(file.path(tempdir(), 'parquet'))

pathling_disconnect(pc)

Write FHIR data to managed tables

Description

Writes the data from a data source to a set of tables in the Spark catalog.

Usage

ds_write_tables(ds, schema = NULL, save_mode = SaveMode$OVERWRITE)

Arguments

Value

No return value, called for side effects only.

See Also

Pathling documentation - Writing managed tables

SaveMode

Other data sink functions: ds_write_delta(), ds_write_ndjson(), ds_write_parquet()

Examples

# Create a temporary warehouse location, which will be used when we call ds_write_tables().
temp_dir_path <- tempfile()
dir.create(temp_dir_path)
sc <- sparklyr::spark_connect(master = "local[*]", config = list(
  "sparklyr.shell.conf" = c(
    paste0("spark.sql.warehouse.dir=", temp_dir_path),
    "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension",
    "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"
  )
), version = pathling_spark_info()$spark_version)

pc <- pathling_connect(sc)
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))

# Write the data to a set of Spark tables in the 'default' database.
data_source %>% ds_write_tables("default", save_mode = SaveMode$MERGE)

pathling_disconnect(pc)
unlink(temp_dir_path, recursive = TRUE)

Create or retrieve the Pathling context

Description

Creates a Pathling context with the given configuration options.

Usage

pathling_connect(
  spark = NULL,
  max_nesting_level = 3,
  enable_extensions = FALSE,
  enabled_open_types = c("boolean", "code", "date", "dateTime", "decimal", "integer",
    "string", "Coding", "CodeableConcept", "Address", "Identifier", "Reference"),
  enable_terminology = TRUE,
  terminology_server_url = "https://tx.ontoserver.csiro.au/fhir",
  terminology_verbose_request_logging = FALSE,
  terminology_socket_timeout = 60000,
  max_connections_total = 32,
  max_connections_per_route = 16,
  terminology_retry_enabled = TRUE,
  terminology_retry_count = 2,
  enable_cache = TRUE,
  cache_max_entries = 2e+05,
  cache_storage_type = StorageType$MEMORY,
  cache_storage_path = NULL,
  cache_default_expiry = 600,
  cache_override_expiry = NULL,
  token_endpoint = NULL,
  enable_auth = FALSE,
  client_id = NULL,
  client_secret = NULL,
  scope = NULL,
  token_expiry_tolerance = 120,
  accept_language = NULL
)

Arguments

Details

If no Spark session is provided and there is not one already present in this process, a new one will be created.

If a SparkSession is not provided, and one is already running within the current process, it will be reused.

It is assumed that the Pathling library API JAR is already on the classpath. If you are running your own cluster, make sure it is on the list of packages.

Value

A Pathling context instance initialized with the specified configuration

See Also

Other context lifecycle functions: pathling_disconnect(), pathling_disconnect_all(), pathling_spark()

Examples

## Not run: 
# Create PathlingContext for an existing Spark connecton.
pc <- pathling_connect(spark = sc)

# Create PathlingContext with a new Spark connection.
pc <- pathling_connect()
spark <- pathling_spark(pc)

## End(Not run)

Disconnect from the Spark session

Description

Disconnects the Spark connection associated with a Pathling context.

Usage

pathling_disconnect(pc)

Arguments

Value

No return value, called for side effects only.

See Also

Other context lifecycle functions: pathling_connect(), pathling_disconnect_all(), pathling_spark()

Disconnect all Spark connections

Description

Disconnect all Spark connections

Usage

pathling_disconnect_all()

Value

No return value, called for side effects only.

See Also

Other context lifecycle functions: pathling_connect(), pathling_disconnect(), pathling_spark()

Encode FHIR JSON or XML to a dataframe

Description

Takes a Spark DataFrame with string representations of FHIR resources in the given column and encodes the resources of the given types as Spark DataFrame.

Usage

pathling_encode(pc, df, resource_name, input_type = NULL, column = NULL)

Arguments

Value

A Spark DataFrame containing the given type of resources encoded into Spark columns.

See Also

Other encoding functions: pathling_encode_bundle()

Examples

pc <- pathling_connect()
json_resources_df <- pathling_spark(pc) %>% 
     sparklyr::spark_read_text(path=system.file('extdata','ndjson', 'Condition.ndjson', 
             package='pathling'))
pc %>% pathling_encode(json_resources_df, 'Condition')
pathling_disconnect(pc)

Encode FHIR Bundles to a dataframe

Description

Takes a dataframe with string representations of FHIR bundles in the given column and outputs a dataframe of encoded resources.

Usage

pathling_encode_bundle(pc, df, resource_name, input_type = NULL, column = NULL)

Arguments

Value

A Spark DataFrame containing the given type of resources encoded into Spark columns.

See Also

Other encoding functions: pathling_encode()

Examples

pc <- pathling_connect()
json_resources_df <- pathling_spark(pc) %>% 
     sparklyr::spark_read_text(path=system.file('extdata','bundle-xml', package='pathling'), 
         whole = TRUE)
pc %>% pathling_encode_bundle(json_resources_df, 'Condition',
     input_type = MimeType$FHIR_XML, column = 'contents')
pathling_disconnect(pc)

Read resource from Pathling example data

Description

Reads a FHIR resource dataframe from the package example data.

Usage

pathling_example_resource(pc, resource_name)

Arguments

Details

The resources are read from the package example data in the extdata/parquet directory. Currently the following resources are available: 'Patient' and 'Condition'.

Value

A Spark DataFrame containing the resource data.

See Also

Other example functions: pathling_examples()

Examples

## Not run: 
pathling_example_resource(pc, 'Condition')

## End(Not run)

Get path to Pathling example data

Description

Construct the path to the package example data in a platform-independent way.

Usage

pathling_examples(...)

Arguments

Value

The path to the examples data.

See Also

Other example functions: pathling_example_resource()

Examples

pathling_examples('ndjson', 'Condition.ndjson')

Install Spark

Description

Installs the version of Spark/Hadoop defined in the package metadata using the sparklyr::spark_install function.

Usage

pathling_install_spark()

Value

List with information about the installed version.

See Also

Other installation functions: pathling_is_spark_installed(), pathling_spark_info(), pathling_version()

Check if Spark is installed

Description

Checks if the version of Spark/Hadoop required by Pathling is installed.

Usage

pathling_is_spark_installed()

Value

TRUE if the required version of Spark/Hadoop is installed, FALSE otherwise.

See Also

Other installation functions: pathling_install_spark(), pathling_spark_info(), pathling_version()

Create a data source from a FHIR Bulk Data Access API endpoint

Description

Creates a data source by downloading data from a FHIR server that implements the FHIR Bulk Data Access API.

Usage

pathling_read_bulk(
  pc,
  fhir_endpoint_url,
  output_dir,
  group_id = NULL,
  patients = NULL,
  types = NULL,
  output_format = "application/fhir+ndjson",
  since = NULL,
  elements = NULL,
  type_filters = NULL,
  include_associated_data = NULL,
  output_extension = "ndjson",
  timeout = NULL,
  max_concurrent_downloads = 10,
  auth_config = NULL
)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading from Bulk Data API

Other data source functions: pathling_read_bundles(), pathling_read_datasets(), pathling_read_delta(), pathling_read_ndjson(), pathling_read_parquet(), pathling_read_tables()

Examples

## Not run: 
pc <- pathling_connect()

# Basic system-level export
data_source <- pc %>% pathling_read_bulk(
  fhir_endpoint_url = "https://bulk-data.smarthealthit.org/fhir",
  output_dir = "/tmp/bulk_export"
)

# Group-level export with filters
data_source <- pc %>% pathling_read_bulk(
  fhir_endpoint_url = "https://bulk-data.smarthealthit.org/fhir", 
  output_dir = "/tmp/bulk_export",
  group_id = "group-1",
  types = c("Patient", "Observation"),
  elements = c("id", "status"),
  since = as.POSIXct("2023-01-01")
)

# Patient-level export with auth
data_source <- pc %>% pathling_read_bulk(
  fhir_endpoint_url = "https://bulk-data.smarthealthit.org/fhir",
  output_dir = "/tmp/bulk_export", 
  patients = c(
    "123",  # Just the ID portion
    "456"
  ),
  auth_config = list(
    enabled = TRUE,
    client_id = "my-client-id",
    private_key_jwk = '{ "kty":"RSA", ...}',
    scope = "system/*.read"
  )
)

pathling_disconnect(pc)

## End(Not run)

Create a data source from FHIR bundles

Description

Creates a data source from a directory containing FHIR bundles.

Usage

pathling_read_bundles(pc, path, resource_types, mime_type = MimeType$FHIR_JSON)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading Bundles

Other data source functions: pathling_read_bulk(), pathling_read_datasets(), pathling_read_delta(), pathling_read_ndjson(), pathling_read_parquet(), pathling_read_tables()

Examples

## Not run: 
data_source <- pc %>% pathling_read_bundles(pathling_examples('bundle-xml'),
     c("Patient", "Observation"), MimeType$FHIR_XML)
data_source %>% ds_read('Observation') %>% sparklyr::sdf_nrow()

## End(Not run)

Create a data source from datasets

Description

Creates an immutable, ad-hoc data source from a named list of Spark datasets indexed with resource type codes.

Usage

pathling_read_datasets(pc, resources)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading datasets

Other data source functions: pathling_read_bulk(), pathling_read_bundles(), pathling_read_delta(), pathling_read_ndjson(), pathling_read_parquet(), pathling_read_tables()

Examples

## Not run: 
patient_df <- pc %>% pathling_example_resource('Patient')
condition_df <- pc %>% pathling_example_resource('Condition')
data_source <- pc %>% pathling_read_datasets(list(Patient = patient_df, Condition = condition_df))
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()

## End(Not run)

Create a data source from Delta tables

Description

pathling_read_delta() creates a data source from a directory containing Delta tables. Each table must be named according to the name of the resource type that it stores.

Usage

pathling_read_delta(pc, path)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading Delta

Other data source functions: pathling_read_bulk(), pathling_read_bundles(), pathling_read_datasets(), pathling_read_ndjson(), pathling_read_parquet(), pathling_read_tables()

Examples

## Not run: 
data_source <- pc %>% pathling_read_delta(pathling_examples('delta'))
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()

## End(Not run)

Create a data source from NDJSON

Description

Creates a data source from a directory containing NDJSON files. The files must be named with the resource type code and must have the ".ndjson" extension, e.g. "Patient.ndjson" or "Observation.ndjson".

Usage

pathling_read_ndjson(pc, path, extension = "ndjson", file_name_mapper = NULL)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading NDJSON

Other data source functions: pathling_read_bulk(), pathling_read_bundles(), pathling_read_datasets(), pathling_read_delta(), pathling_read_parquet(), pathling_read_tables()

Examples

## Not run: 
data_source <- pc %>% pathling_read_ndjson(pathling_examples('ndjson'))
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()

## End(Not run)

Create a data source from Parquet tables

Description

pathling_read_parquet() creates a data source from a directory containing Parquet tables. Each table must be named according to the name of the resource type that it stores.

Usage

pathling_read_parquet(pc, path)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading Parquet

Other data source functions: pathling_read_bulk(), pathling_read_bundles(), pathling_read_datasets(), pathling_read_delta(), pathling_read_ndjson(), pathling_read_tables()

Examples

## Not run: 
data_source <- pc %>% pathling_read_parquet(pathling_examples('parquet'))
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()

## End(Not run)

Create a data source from managed tables

Description

pathling_read_tables() creates a data source from a set of Spark tables, where the table names are the resource type codes.

Usage

pathling_read_tables(pc, schema = NULL)

Arguments

Value

A DataSource object that can be used to run queries against the data.

See Also

Pathling documentation - Reading managed tables

Other data source functions: pathling_read_bulk(), pathling_read_bundles(), pathling_read_datasets(), pathling_read_delta(), pathling_read_ndjson(), pathling_read_parquet()

Examples

## Not run: 
data_source <- pc %>% pathling_read_tables()
data_source %>% ds_read('Patient') %>% sparklyr::sdf_nrow()

## End(Not run)

Get the Spark session

Description

Returns the Spark connection associated with a Pathling context.

Usage

pathling_spark(pc)

Arguments

Value

The Spark connection associated with this Pathling context.

See Also

Other context lifecycle functions: pathling_connect(), pathling_disconnect(), pathling_disconnect_all()

Get versions of Spark and other dependencies

Description

Returns the versions of Spark and Spark packages used by the Pathling R library.

Usage

pathling_spark_info()

Value

A list containing the following keys:

• spark_version: The version of Spark used by Pathling.

• scala_version: The version of Scala used by Pathling.

• hadoop_version: The version of Hadoop used by Pathling.

• hadoop_major_version: The major version of Hadoop used by Pathling.

• delta_version: The version of Delta used by Pathling.

See Also

Other installation functions: pathling_install_spark(), pathling_is_spark_installed(), pathling_version()

Get version of Pathling

Description

Get version of Pathling

Usage

pathling_version()

Value

The version of the Pathling R library.

See Also

Other installation functions: pathling_install_spark(), pathling_is_spark_installed(), pathling_spark_info()

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

sparklyr

%>%

Convert a vector to a SQL array literal

Description

Converts a vector to an expression with the corresponding SQL array literal.

Usage

to_array(value)

Arguments

Value

The quosure with the SQL array literal that can be used in dplyr::mutate.

Get designations for codings

Description

Takes a Coding column as its input. Returns a Column that contains the values of designations (strings) for this coding that match the specified use and language. If the language is not provided, then all designations with the specified type are returned regardless of their language.

Usage

tx_designation(coding, use = NULL, language = NULL)

Arguments

Value

The Column containing the result of the operation (array of strings with designation values).

See Also

Pathling documentation - Retrieving designations

Examples

## Not run: 
# Get the (first) SNOMED CT "Fully specified name" ('900000000000003001')  
# for the first coding of the Condition resource, in the 'en' language.
pc %>% pathling_example_resource('Condition') %>%
     sparklyr::mutate(
            id, 
            designation = (!!tx_designation(code[['coding']][[0]], 
                     !!tx_to_snomed_coding('900000000000003001'), language = 'en'))[[0]], 
            .keep='none')

## End(Not run)

Get the display text for codings

Description

Takes a Coding column as its input. Returns a Column that contains the canonical display name associated with the given code.

Usage

tx_display(coding, accept_language = NULL)

Arguments

Value

A Column containing the result of the operation (String).

See Also

Pathling documentation - Multi-language support

Other terminology functions: tx_member_of(), tx_property_of(), tx_subsumed_by(), tx_subsumes(), tx_translate()

Examples

## Not run: 
# Get the display name of the first coding of the Condition resource, with the default language.
pc %>% pathling_example_resource('Condition') %>%
     sparklyr::mutate(
         id, 
         display = !!tx_display(code[['coding']][[0]]), 
         .keep='none')

## End(Not run)

Test membership within a value set

Description

Takes a Coding or array of Codings column as its input. Returns the column which contains a Boolean value, indicating whether any of the input Codings is a member of the specified FHIR ValueSet.

Usage

tx_member_of(codings, value_set_uri)

Arguments

Value

A Column containing the result of the operation.

See Also

Pathling documentation - Value set membership

Other terminology functions: tx_display(), tx_property_of(), tx_subsumed_by(), tx_subsumes(), tx_translate()

Get properties for codings

Description

Takes a Coding column as its input. Returns a Column that contains the values of properties for this coding with specified names and types. The type of the result column depends on the types of the properties. Primitive FHIR types are mapped to their corresponding SQL primitives. Complex types are mapped to their corresponding structs.

Usage

tx_property_of(
  coding,
  property_code,
  property_type = "string",
  accept_language = NULL
)

Arguments

Value

The Column containing the result of the operation (array of property values).

See Also

PropertyType

Pathling documentation - Retrieving properties

Other terminology functions: tx_display(), tx_member_of(), tx_subsumed_by(), tx_subsumes(), tx_translate()

Examples

## Not run: 
# Get the (first) value of the `inactive` property of the first coding of the Condition resource.
pc %>% pathling_example_resource('Condition') %>%
     sparklyr::mutate(id, 
         is_inavtive = (!!tx_property_of(code[['coding']][[0]], 
                                 "inactive",PropertyType$BOOLEAN))[[0]], 
         .keep='none'
     )

## End(Not run)

Test subsumption between codings

Description

Takes two Coding columns as input. Returns a Column that contains a Boolean value, indicating whether the left Coding is subsumed by the right Coding.

Usage

tx_subsumed_by(left_codings, right_codings)

Arguments

Value

A Column containing the result of the operation (boolean).

See Also

Pathling documentation - Subsumption testing

Other terminology functions: tx_display(), tx_member_of(), tx_property_of(), tx_subsumes(), tx_translate()

Examples

pc <- pathling_connect()

# Test the codings of the Condition `code` for subsumption by a SNOMED CT code.
pc %>% pathling_example_resource('Condition') %>%
    sparklyr::mutate(
         id,
         is_subsumed_by = !!tx_subsumed_by(code[['coding']],
             !!tx_to_snomed_coding('444814009')),
         .keep='none')
 
pathling_disconnect(pc)

Test subsumption between codings

Description

Takes two Coding columns as input. Returns a Column that contains a Boolean value, indicating whether the left Coding subsumes the right Coding.

Usage

tx_subsumes(left_codings, right_codings)

Arguments

Value

A Column containing the result of the operation (boolean).

See Also

Pathling documentation - Subsumption testing

Other terminology functions: tx_display(), tx_member_of(), tx_property_of(), tx_subsumed_by(), tx_translate()

Examples

## Not run: 
# Test the codings of the Condition `code` for subsumption of a SNOMED CT code.
pc %>% pathling_example_resource('Condition') %>%
    sparklyr::mutate(
         id,
         subsumes = !!tx_subsumes(code[['coding']],
             !!tx_to_snomed_coding('444814009')),
         .keep='none')

## End(Not run)

Convert codes to Coding structures

Description

Converts a Column containing codes into a Column that contains a Coding struct.

Usage

tx_to_coding(coding_column, system, version = NULL)

Arguments

Details

The Coding struct Column can be used as an input to terminology functions such as tx_member_of and tx_translate. Please note that inside sparklyr verbs such as mutate the functions calls need to be preceded with !!, e.g: !!tx_to_coding(CODE, SNOMED_URI).

Value

A Column containing a Coding struct.

See Also

FHIR R4 - Coding

Other terminology helpers: tx_to_ecl_value_set(), tx_to_loinc_coding(), tx_to_snomed_coding()

Examples

## Not run: 
condition_df <- pathling_spark(pc) %>% sparklyr::copy_to(conditions)

# Convert codes to ICD-10 codings.
condition_df %>% sparklyr::mutate(
    icdCoding = !!tx_to_coding(CODE, "http://hl7.org/fhir/sid/icd-10"), .keep = 'none'
)

## End(Not run)

Convert a SNOMED CT ECL expression to a ValueSet URI

Description

Converts a SNOMED CT ECL expression into a FHIR ValueSet URI. It can be used with the 'tx_member_of function.

Usage

tx_to_ecl_value_set(ecl)

Arguments

Value

The ValueSet URI.

See Also

Using SNOMED CT with HL7 Standards - Implicit Value Sets

Other terminology helpers: tx_to_coding(), tx_to_loinc_coding(), tx_to_snomed_coding()

Examples

## Not run: 
# Example usage of tx_to_ecl_value_set function
tx_to_ecl_value_set('<<373265006 |Analgesic (substance)|')

## End(Not run)

Convert LOINC codes to Coding structures

Description

Converts a Column containing codes into a Column that contains a LOINC Coding struct.

Usage

tx_to_loinc_coding(coding_column, version = NULL)

Arguments

Details

The Coding struct Column can be used as an input to terminology functions such as tx_member_of and tx_translate. Please note that inside sparklyr verbs such as mutate the functions calls need to be preceded with !!, e.g: !!tx_to_coding(CODE, SNOMED_URI).

Value

A Column containing a Coding struct.

See Also

Other terminology helpers: tx_to_coding(), tx_to_ecl_value_set(), tx_to_snomed_coding()

Examples

## Not run: 
condition_df <- pathling_spark(pc) %>% sparklyr::copy_to(conditions)

# Convert codes to LOINC codings.
# Equivalent to: tx_to_coding(CODE, "http://loinc.org")
condition_df %>% sparklyr::mutate(loincCoding = !!tx_to_loinc_coding(CODE), .keep = 'none')

## End(Not run)

Convert SNOMED CT codes to Coding structures

Description

Converts a Column containing codes into a Column that contains a SNOMED Coding struct.

Usage

tx_to_snomed_coding(coding_column, version = NULL)

Arguments

Details

The Coding struct Column can be used as an input to terminology functions such as tx_member_of and tx_translate. Please note that inside sparklyr verbs such as mutate the functions calls need to be preceded with !!, e.g: !!tx_to_coding(CODE, SNOMED_URI).

Value

A Column containing a Coding struct.

See Also

Other terminology helpers: tx_to_coding(), tx_to_ecl_value_set(), tx_to_loinc_coding()

Examples

## Not run: 
condition_df <- pathling_spark(pc) %>% sparklyr::copy_to(conditions)

# Convert codes to SNOMED CT codings.
# Equivalent to: tx_to_coding(CODE, "http://snomed.info/sct")
condition_df %>% sparklyr::mutate(snomedCoding = !!tx_to_snomed_coding(CODE), .keep = 'none')

## End(Not run)

Translate between value sets

Description

Takes a Coding column as input. Returns the Column which contains an array of Coding value with translation targets from the specified FHIR ConceptMap. There may be more than one target concept for each input concept. Only the translation with the specified equivalences are returned.

Usage

tx_translate(
  codings,
  concept_map_uri,
  reverse = FALSE,
  equivalences = NULL,
  target = NULL
)

Arguments

Value

A Column containing the result of the operation (an array of Coding structs).

See Also

Equivalence

Pathling documentation - Concept translation

Other terminology functions: tx_display(), tx_member_of(), tx_property_of(), tx_subsumed_by(), tx_subsumes()

Examples

## Not run: 
# Translates the codings of the Condition `code` using a SNOMED implicit concept map.
pc %>% pathling_example_resource('Condition') %>%
    sparklyr::mutate(
         id,
         translation = !!tx_translate(code[['coding']],
                 'http://snomed.info/sct?fhir_cm=900000000000527005'),
         .keep='none')

## End(Not run)