Using LoincHPO

Simple Use Case

1. Parse the annotations from (https://github.com/TheJacksonLaboratory/loinc2hpoAnnotation) using AnnotationParser:

   # If you loaded the annotations into a pandas df:
   annotations = AnnotationParser.parse_annotations(dataframe)
   
   # If you simply have the file:
   annotations = AnnotationParser.parse_annotation_file(annotation_path)
   

2. Create your queries:

   # From a file:
   queries = QueryFileParser.parse(query_path)
   # A single query:
   query = Query(loinc_id, outcome)
   

3. Resolve the hpo term:

   resolver = QueryResolver(annotations)
   hpo_term = resolver.resolve(query)
   

OMOP Common Data Model (Requires Spark)

For a more detailed description on the way we transform OMOP measurements to Human Phenotype Ontology terms visit here. You will need the major tables Concept, Concept Synonym, Measurement.

If you have the tables in a file format use:

parser = ClinicalTableParser()
concept = parser.parse_table("/some/file/path", ClinicalTableName.CONCEPT, your_spark_session)
... the 2 other tables
df = OMOPTransformer.transform(concept, concept_synonym, measurement)

If you have the tables in spark df already:

df = OMOPTransformer.transform(concept, concept_synonym, measurement)