Terminology functions

The library also provides a set of functions for querying a FHIR terminology server from within your queries and transformations.

Value set membership

The member_of function can be used to test the membership of a code within a FHIR value set. This can be used with both explicit value sets (i.e. those that have been pre-defined and loaded into the terminology server) and implicit value sets (e.g. SNOMED CT Expression Constraint Language).

In this example, we take a list of SNOMED CT diagnosis codes and create a new column which shows which are viral infections. We use an ECL expression to define viral infection as a disease with a pathological process of "Infectious process", and a causative agent of "Virus".

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext, to_snomed_coding, to_ecl_value_set,
    member_of

pc = PathlingContext.create()
csv = pc.spark.read.csv("conditions.csv")

VIRAL_INFECTION_ECL = """
    << 64572001|Disease| : (
      << 370135005|Pathological process| = << 441862004|Infectious process|,
      << 246075003|Causative agent| = << 49872002|Virus|
    )
"""

csv.select(
    "CODE",
    "DESCRIPTION",
    member_of(
        to_snomed_coding(csv.CODE),
        to_ecl_value_set(VIRAL_INFECTION_ECL)
    ).alias("VIRAL_INFECTION"),
).show()

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext
import au.csiro.pathling.sql.Terminology._
import au.csiro.pathling.library.TerminologyHelpers._

val pc = PathlingContext.create()
val csv = pc.getSpark.read.csv("conditions.csv")

val VIRAL_INFECTION_ECL = """
    << 64572001|Disease| : (
      << 370135005|Pathological process| = << 441862004|Infectious process|,
      << 246075003|Causative agent| = << 49872002|Virus|
    )
"""

csv.select(
    csv.col("CODE"),
    csv.col("DESCRIPTION"),
    member_of(toCoding(csv.col("CODE"), "http://snomed.info/sct"),
        toEclValueSet(VIRAL_INFECTION_ECL)).alias("VIRAL_INFECTION")
).show()

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import static au.csiro.pathling.library.TerminologyHelpers.*;
import static au.csiro.pathling.sql.Terminology.*;

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

class MyApp {
    public static void main(String[] args) {
        PathlingContext pc = PathlingContext.create();
        Dataset<Row> csv = pc.getSpark().read().csv("conditions.csv");
        
        String VIRAL_INFECTION_ECL = """
            << 64572001|Disease| : (
              << 370135005|Pathological process| = << 441862004|Infectious process|,
              << 246075003|Causative agent| = << 49872002|Virus|
            )
        """;

        csv.select(
                csv.col("CODE"),
                csv.col("DESCRIPTION"),
                member_of(toSnomedCoding(csv.col("CODE")),
                        toEclValueSet(VIRAL_INFECTION_ECL)).alias("VIRAL_INFECTION")
        ).show();
    }
}

Results in:

CODE	DESCRIPTION	VIRAL_INFECTION
65363002	Otitis media	false
16114001	Fracture of ankle	false
444814009	Viral sinusitis	true
444814009	Viral sinusitis	true
43878008	Streptococcal sore throat	false

Concept translation

The translate function can be used to translate codes from one code system to another using maps that are known to the terminology server. In this example, we translate our SNOMED CT diagnosis codes into Read CTV3.

Please note that the type of the output column is the array of coding structs, as the translation may produce multiple results for each input coding.

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext, to_snomed_coding, translate
from pyspark.sql.functions import explode_outer

pc = PathlingContext.create()
csv = pc.spark.read.csv("conditions.csv")

translate_result = csv.withColumn(
    "READ_CODES",
    translate(
        to_snomed_coding(csv.CODE),
        concept_map_uri="http://snomed.info/sct/900000000000207008?"
                        "fhir_cm=900000000000497000",
    ).code,
)
translate_result.select(
    "CODE", "DESCRIPTION", explode_outer("READ_CODES").alias("READ_CODE")
).show()

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext
import au.csiro.pathling.sql.Terminology._
import au.csiro.pathling.library.TerminologyHelpers._
import org.apache.spark.sql.functions.explode_outer

val pc = PathlingContext.create()
val csv = spark.read.csv("conditions.csv")

val translate_result = csv.withColumn(
    "READ_CODES",
    translate(
        toCoding(csv.col("CODE"), "https://snomed.info/sct"),
        "http://snomed.info/sct/900000000000207008?fhir_cm=900000000000497000",
        false, null
    ).getField("code")
)
translate_result.select(
    csv.col("CODE"), csv.col("DESCRIPTION"), explode_outer(translate_result.col("READ_CODES")).alias("READ_CODE")
).show()

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import static au.csiro.pathling.sql.Terminology.*;
import static au.csiro.pathling.library.TerminologyHelpers.*;
import static org.apache.spark.sql.functions.explode_outer;

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

class MyApp {
    public static void main(String[] args) {
        PathlingContext pc = PathlingContext.create();
        Dataset<Row> csv = pc.getSpark().read().csv("conditions.csv");

        val translate_result = csv.withColumn(
                "READ_CODES",
                translate(
                        toCoding(csv.col("CODE"), "https://snomed.info/sct"),
                        "http://snomed.info/sct/900000000000207008?fhir_cm=900000000000497000",
                        false, null
                ).getField("code")
        );
        translate_result.select(
                csv.col("CODE"), csv.col("DESCRIPTION"), 
                explode_outer(translate_result.col("READ_CODES")).alias("READ_CODE")
        ).show();
    }
}

Results in:

CODE	DESCRIPTION	READ_CODE
65363002	Otitis media	[X00ik]
16114001	Fracture of ankle	[S34..]
444814009	Viral sinusitis	[XUjp0]
444814009	Viral sinusitis	[XUjp0]
43878008	Streptococcal sore throat	[A340.]

Subsumption testing

Subsumption test is a fancy way of saying "is this code equal or a subtype of this other code".

For example, a code representing "ankle fracture" is subsumed by another code representing "fracture". The "fracture" code is more general, and using it with subsumption can help us find other codes representing different subtypes of fracture.

The subsumes function allows us to perform subsumption testing on codes within our data. The order of the left and right operands can be reversed to query whether a code is "subsumed by" another code.

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext, Coding, to_snomed_coding, subsumes

pc = PathlingContext.create()
csv = pc.spark.read.csv("conditions.csv")

# 232208008 |Ear, nose and throat disorder|
left_coding = Coding('http://snomed.info/sct', '232208008')
right_coding_column = to_snomed_coding(csv.CODE)

csv.select(
    'CODE', 'DESCRIPTION',
    subsumes(left_coding, right_coding_column).alias('SUBSUMES')
).show()

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext
import au.csiro.pathling.sql.Terminology._
import au.csiro.pathling.library.TerminologyHelpers._
import au.csiro.pathling.fhirpath.encoding.CodingEncoding

val pc = PathlingContext.create()
val csv = spark.read.csv("conditions.csv")

csv.select(
    csv.col("CODE"),
    // 232208008 |Ear, nose and throat disorder|
    subsumes(
        CodingEncoding.toStruct(
            lit(null),
            lit(SNOMED_URI),
            lit(null),
            lit("232208008"),
            lit(null),
            lit(null)
        ), 
        toSnomedCoding(csv.col("CODE"))
    ).alias("IS_ENT")
).show()

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import static au.csiro.pathling.sql.Terminology.*;
import static au.csiro.pathling.library.TerminologyHelpers.*;

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

class MyApp {
    public static void main(String[] args) {
        PathlingContext pc = PathlingContext.create();
        Dataset<Row> csv = pc.getSpark().read().csv("conditions.csv");

        csv.select(
                csv.col("CODE"),
                // 232208008 |Ear, nose and throat disorder|
                subsumes(
                        CodingEncoding.toStruct(
                                lit(null),
                                lit(SNOMED_URI),
                                lit(null),
                                lit("232208008"),
                                lit(null),
                                lit(null)
                        ),
                        toSnomedCoding(csv.col("CODE"))
                ).alias("IS_ENT")
        ).show();
    }
}

Results in:

CODE	DESCRIPTION	IS_ENT
65363002	Otitis media	true
16114001	Fracture of ankle	false
444814009	Viral sinusitis	true

Retrieving properties

Some terminologies contain additional properties that are associated with codes. You can query these properties using the property_of function.

There is also a display function that can be used to retrieve the preferred display term for each code.

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext, to_snomed_coding, property_of, display,
    PropertyType

pc = PathlingContext.create()
csv = pc.spark.read.csv("conditions.csv")

# Get the parent codes for each code in the dataset.
parents = csv.withColumn(
    "PARENTS",
    property_of(to_snomed_coding(csv.CODE), "parent", PropertyType.CODE),
)
# Split each parent code into a separate row.
exploded_parents = parents.selectExpr(
    "CODE", "DESCRIPTION", "explode_outer(PARENTS) AS PARENT"
)
# Retrieve the preferred term for each parent code.
with_displays = exploded_parents.withColumn(
    "PARENT_DISPLAY", display(to_snomed_coding(exploded_parents.PARENT))
)
with_displays.show()

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext
import au.csiro.pathling.sql.Terminology
import au.csiro.pathling.sql.Terminology._
import au.csiro.pathling.library.TerminologyHelpers._
import au.csiro.pathling.fhirpath.encoding.CodingEncoding

val pc = PathlingContext.create()
val csv = spark.read.csv("conditions.csv")

// Get the parent codes for each code in the dataset.
val parents = csv.withColumn(
    "PARENTS",
    property_of(toSnomedCoding(csv.col("CODE")), "parent", "code")
)
// Split each parent code into a separate row.
val exploded_parents = parents.selectExpr(
    "CODE", "DESCRIPTION", "explode_outer(PARENTS) AS PARENT"
)
// Retrieve the preferred term for each parent code.
val with_displays = exploded_parents.withColumn(
    "PARENT_DISPLAY", Terminology.display(toSnomedCoding(exploded_parents.col("PARENT")))
)
with_displays.show()

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import static au.csiro.pathling.sql.Terminology.*;
import static au.csiro.pathling.library.TerminologyHelpers.*;

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

class MyApp {
    public static void main(String[] args) {
        PathlingContext pc = PathlingContext.create();
        Dataset<Row> csv = pc.getSpark().read().csv("conditions.csv");

        // Get the parent codes for each code in the dataset.
        Dataset<Row> parents = csv.withColumn(
                "PARENTS",
                property_of(toSnomedCoding(csv.col("CODE")), "parent", "code")
        );
        // Split each parent code into a separate row.
        Dataset<Row> exploded_parents = parents.selectExpr(
                "CODE", "DESCRIPTION", "explode_outer(PARENTS) AS PARENT"
        );
        // Retrieve the preferred term for each parent code.
        Dataset<Row> with_displays = exploded_parents.withColumn(
                "PARENT_DISPLAY", Terminology.display(toSnomedCoding(exploded_parents.col("PARENT")))
        );
        with_displays.show();
    }
}

Results in:

CODE	DESCRIPTION	PARENT	PARENT_DISPLAY
65363002	Otitis media	43275000	Otitis
65363002	Otitis media	68996008	Disorder of middle ear
16114001	Fracture of ankle	125603006	Injury of ankle
16114001	Fracture of ankle	46866001	Fracture of lower limb
444814009	Viral sinusitis	36971009	Sinusitis
444814009	Viral sinusitis	281794004	Viral upper respiratory tract infection
444814009	Viral sinusitis	363166002	Infective disorder of head
444814009	Viral sinusitis	36971009	Sinusitis
444814009	Viral sinusitis	281794004	Viral upper respiratory tract infection
444814009	Viral sinusitis	363166002	Infective disorder of head

Retrieving designations

Some terminologies contain additional display terms for codes. These can be used for language translations, synonyms, and more. You can query these terms using the designation function.

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext, to_snomed_coding, Coding, designation

pc = PathlingContext.create()
csv = pc.spark.read.csv("conditions.csv")

# Get the synonyms for each code in the dataset.
synonyms = csv.withColumn(
    "SYNONYMS",
    designation(to_snomed_coding(csv.CODE), Coding.of_snomed("900000000000013009")),
)
# Split each synonyms into a separate row.
exploded_synonyms = synonyms.selectExpr(
    "CODE", "DESCRIPTION", "explode_outer(SYNONYMS) AS SYNONYM"
)
exploded_synonyms.show()

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext
import au.csiro.pathling.sql.Terminology._
import au.csiro.pathling.library.TerminologyHelpers._
import org.hl7.fhir.r4.model.Coding

val pc = PathlingContext.create()
val csv = spark.read.csv("conditions.csv")

// Get the synonyms for each code in the dataset.
val synonyms = csv.withColumn(
    "SYNONYMS",
    designation(toSnomedCoding(csv.col("CODE")),
        new Coding("http://snomed.info/sct", "900000000000013009", null))
)
// Split each synonym into a separate row.
val exploded_synonyms = synonyms.selectExpr(
    "CODE", "DESCRIPTION", "explode_outer(SYNONYMS) AS SYNONYM"
)
exploded_synonyms.show()

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import static au.csiro.pathling.sql.Terminology.*;
import static au.csiro.pathling.library.TerminologyHelpers.*;

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

class MyApp {

    public static void main(String[] args) {
        PathlingContext pc = PathlingContext.create();
        Dataset<Row> csv = pc.getSpark().read().csv("conditions.csv");

        // Get the synonyms for each code in the dataset.
        Dataset<Row> synonyms = csv.withColumn(
                "SYNONYMS",
                designation(toSnomedCoding(csv.col("CODE")),
                        new Coding("http://snomed.info/sct",
                                "900000000000013009", null))
        );
        // Split each synonym into a separate row.
        Dataset<Row> exploded_synonyms = synonyms.selectExpr(
                "CODE", "DESCRIPTION", "explode_outer(SYNONYMS) AS SYNONYM"
        );
        exploded_synonyms.show();
    }
}

Results in:

CODE	DESCRIPTION	SYNONYM
65363002	Otitis media	OM - Otitis media
16114001	Fracture of ankle	Ankle fracture
16114001	Fracture of ankle	Fracture of distal end of tibia and fibula
444814009	Viral sinusitis (disorder)	NULL
444814009	Viral sinusitis (disorder)	NULL
43878008	Streptococcal sore throat (disorder)	Septic sore throat
43878008	Streptococcal sore throat (disorder)	Strep throat
43878008	Streptococcal sore throat (disorder)	Strept throat
43878008	Streptococcal sore throat (disorder)	Streptococcal angina
43878008	Streptococcal sore throat (disorder)	Streptococcal pharyngitis

Authentication

Pathling can be configured to connect to a protected terminology server by supplying a set of OAuth2 client credentials and a token endpoint.

Here is an example of how to authenticate to the NHS terminology server:

Python
Scala
Java

To use the Pathling encoders from Python, install the pathling package using pip. Note that Java 11 is required, with your JAVA_HOME properly set.

from pathling import PathlingContext

pc = PathlingContext.create(
    terminology_server_url='https://ontology.nhs.uk/production1/fhir',
    token_endpoint='https://ontology.nhs.uk/authorisation/auth/realms/nhs-digital-terminology/protocol/openid-connect/token',
    client_id='[client ID]',
    client_secret='[client secret]'
)

To use the Pathling encoders from Scala, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.{PathlingContext, PathlingContextConfiguration}

val config = PathlingContextConfiguration.builder()
        .terminologyServerUrl("https://ontology.nhs.uk/production1/fhir")
        .tokenEndpoint("https://ontology.nhs.uk/authorisation/auth/realms/nhs-digital-terminology/protocol/openid-connect/token")
        .clientId("[client ID]")
        .clientSecret("[client secret]")
        .build()
val pc = PathlingContext.create(config)

To use the Pathling encoders from Java, install the au.csiro.pathling:encoders Maven package.

import au.csiro.pathling.library.PathlingContext;
import au.csiro.pathling.library.PathlingContextConfiguration;

class MyApp {
    public static void main(String[] args) {
        PathlingContextConfiguration config = PathlingContextConfiguration.builder()
                .terminologyServerUrl("https://ontology.nhs.uk/production1/fhir")
                .tokenEndpoint("https://ontology.nhs.uk/authorisation/auth/realms/nhs-digital-terminology/protocol/openid-connect/token")
                .clientId("[client ID]")
                .clientSecret("[client secret]")
                .build();
        PathlingContext pc = PathlingContext.create(config);
        // ...
    }
}

Terminology functions

Value set membership​

Concept translation​

Subsumption testing​

Retrieving properties​

Retrieving designations​

Authentication​

Value set membership

Concept translation

Subsumption testing

Retrieving properties

Retrieving designations

Authentication