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Pathling Python API - Terminology Functions
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| "## Pathling Python API - Terminology functions\n", | |
| "\n", | |
| "The Pathling Python library provides the ability to use a FHIR terminology server to resolve terminology queries from within Spark. This capability can be used with any Spark dataframe, not just those that have been created from FHIR data." | |
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| "from pathling import PathlingContext\n", | |
| "\n", | |
| "pc = PathlingContext.create()" | |
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| "csv = pc.spark.read.options(header=True).csv(\n", | |
| " 's3://pathling-demo/staging/synthea/10k_csv_20210818/csv/conditions.csv')" | |
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| "### Value set membership\n", | |
| "\n", | |
| "The `member_of` function can be used to test the membership of a code within a FHIR value set. In this example, we take a list of SNOMED CT diagnosis codes and create a new column which shows which are nasal sinus infections." | |
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| "from pathling.functions import to_coding\n", | |
| "\n", | |
| "# << 64572001|Disease| : (\n", | |
| "# << 370135005|Pathological process| = << 441862004|Infectious process|,\n", | |
| "# << 246075003|Causative agent| = << 49872002|Virus|\n", | |
| "# )\n", | |
| "member_of_result = pc.member_of(csv, to_coding(csv.CODE, 'http://snomed.info/sct'),\n", | |
| " 'http://snomed.info/sct?fhir_vs=ecl/%3C%3C%2064572001%20%3A%20('\n", | |
| " '%3C%3C%20370135005%20%3D%20%3C%3C%20441862004%20%2C%20%3C%3C'\n", | |
| " '%20246075003%20%3D%20%3C%3C%2049872002%20)',\n", | |
| " 'VIRAL_INFECTION')\n", | |
| "member_of_result.select('CODE', 'DESCRIPTION', 'VIRAL_INFECTION').toPandas()" | |
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| "### Concept translation\n", | |
| "\n", | |
| "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." | |
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| "translate_result = pc.translate(csv, to_coding(csv.CODE, 'http://snomed.info/sct'),\n", | |
| " 'http://snomed.info/sct?fhir_cm=900000000000497000',\n", | |
| " output_column_name='READ_CODE')\n", | |
| "translate_result = translate_result.withColumn('READ_CODE', translate_result.READ_CODE.code)\n", | |
| "translate_result.select('CODE', 'DESCRIPTION', 'READ_CODE').toPandas()" | |
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| "### Subsumption testing\n", | |
| "\n", | |
| "The `subsumes` function allows us to perform subsumption testing on codes within our data. In hierarchical code systems, a subsumption test determines whether a code is a subtype of another code, e.g. an \"ankle fracture\" is subsumed by \"fracture\".\n", | |
| "\n", | |
| "In this example, we first take our codes, cross-join them and then test whether they subsume each other. Then we do another subsumption test against the \"fracture\" concept." | |
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| "from pathling.coding import Coding\n", | |
| "\n", | |
| "first_3 = csv.limit(3)\n", | |
| "cross_join = first_3.selectExpr('CODE as LEFT', 'DESCRIPTION as LEFT_DESCRIPTION') \\\n", | |
| " .crossJoin(first_3.selectExpr('CODE as RIGHT', 'DESCRIPTION as RIGHT_DESCRIPTION'))\n", | |
| "\n", | |
| "subsumes_result_1 = pc.subsumes(cross_join, 'SUBSUMES',\n", | |
| " left_coding_column=to_coding(cross_join.LEFT, 'http://snomed.info/sct'),\n", | |
| " right_coding_column=to_coding(cross_join.RIGHT, 'http://snomed.info/sct'))\n", | |
| "subsumes_result_2 = pc.subsumes(subsumes_result_1, 'LEFT_IS_ENT',\n", | |
| " # 232208008 |Ear, nose and throat disorder|\n", | |
| " left_coding=Coding('http://snomed.info/sct', '232208008'),\n", | |
| " right_coding_column=to_coding(cross_join.LEFT, 'http://snomed.info/sct'))\n", | |
| "subsumes_result_2.select('LEFT', 'RIGHT', 'LEFT_DESCRIPTION', 'RIGHT_DESCRIPTION', 'SUBSUMES', \n", | |
| " 'LEFT_IS_ENT').toPandas()\n" | |
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