Healthcare NLP v3.0.3 Release Notes

 

3.0.3

We are glad to announce that Spark NLP for Healthcare 3.0.3 has been released!

Highlights

  • Five new entity resolution models to cover UMLS, HPO and LIONC terminologies.
  • New feature for random displacement of dates on deidentification model.
  • Five new pretrained pipelines to map terminologies across each other (from UMLS to ICD10, from RxNorm to MeSH etc.)
  • AnnotationToolReader support for Spark 2.3. The tool that helps model training on Spark-NLP to leverage data annotated using JSL Annotation Tool now has support for Spark 2.3.
  • Updated documentation (Scaladocs) covering more APIs, and examples.

Five new resolver models:

  • sbiobertresolve_umls_major_concepts: This model returns CUI (concept unique identifier) codes for Clinical Findings, Medical Devices, Anatomical Structures and Injuries & Poisoning terms.
  • sbiobertresolve_umls_findings: This model returns CUI (concept unique identifier) codes for 200K concepts from clinical findings.
  • sbiobertresolve_loinc: Map clinical NER entities to LOINC codes using sbiobert.
  • sbluebertresolve_loinc: Map clinical NER entities to LOINC codes using sbluebert.

  • sbiobertresolve_HPO: This model returns Human Phenotype Ontology (HPO) codes for phenotypic abnormalities encountered in human diseases. It also returns associated codes from the following vocabularies for each HPO code:

      * MeSH (Medical Subject Headings)
  * SNOMED
  * UMLS (Unified Medical Language System )
  * ORPHA (international reference resource for information on rare diseases and orphan drugs)
  * OMIM (Online Mendelian Inheritance in Man)

    Related Notebook: Resolver Models

New feature on Deidentification Module

  • isRandomDateDisplacement(True): Be able to apply a random displacement on obfuscation dates. The randomness is based on the seed.
  • Fix random dates when the format is not correct. Now you can repeat an execution using a seed for dates. Random dates will be based on the seed.

Five new healthcare code mapping pipelines:

  • icd10cm_umls_mapping: This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text data. You’ll just feed white space-delimited ICD10CM codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping.

    {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
    'umls': ['C4721411', 'C0159076', 'C0004044']}

  • mesh_umls_mapping: This pretrained pipeline maps MeSH codes to UMLS codes without using any text data. You’ll just feed white space-delimited MeSH codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping.

    {'mesh': ['C028491', 'D019326', 'C579867'],
   'umls': ['C0970275', 'C0886627', 'C3696376']}

  • rxnorm_umls_mapping: This pretrained pipeline maps RxNorm codes to UMLS codes without using any text data. You’ll just feed white space-delimited RxNorm codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping.

    {'rxnorm': ['1161611', '315677', '343663'],
   'umls': ['C3215948', 'C0984912', 'C1146501']}

  • rxnorm_mesh_mapping: This pretrained pipeline maps RxNorm codes to MeSH codes without using any text data. You’ll just feed white space-delimited RxNorm codes and it will return the corresponding MeSH codes as a list. If there is no mapping, the original code is returned with no mapping.

    {'rxnorm': ['1191', '6809', '47613'],
   'mesh': ['D001241', 'D008687', 'D019355']}

  • snomed_umls_mapping: This pretrained pipeline maps SNOMED codes to UMLS codes without using any text data. You’ll just feed white space-delimited SNOMED codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping.

    {'snomed': ['733187009', '449433008', '51264003'],
   'umls': ['C4546029', 'C3164619', 'C0271267']}

    Related Notebook: Healthcare Code Mapping

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