Spark NLP for Healthcare Release Notes 2.6.0

 

2.6.0

Overview

We are honored to announce that Spark NLP Enterprise 2.6.0 has been released. The first time ever, we release three pretrained clinical pipelines to save you from building pipelines from scratch. Pretrained pipelines are already fitted using certain annotators and transformers according to various use cases. The first time ever, we are releasing 3 licensed German models for healthcare and Legal domains.

Models

Pretrained Pipelines:

The first time ever, we release three pretrained clinical pipelines to save you from building pipelines from scratch. Pretrained pipelines are already fitted using certain annotators and transformers according to various use cases and you can use them as easy as follows:

pipeline = PretrainedPipeline('explain_clinical_doc_carp', 'en', 'clinical/models')

pipeline.annotate('my string')

Pipeline descriptions:

  • explain_clinical_doc_carp a pipeline with ner_clinical, assertion_dl, re_clinical and ner_posology. It will extract clinical and medication entities, assign assertion status and find relationships between clinical entities.

  • explain_clinical_doc_era a pipeline with ner_clinical_events, assertion_dl and re_temporal_events_clinical. It will extract clinical entities, assign assertion status and find temporal relationships between clinical entities.

  • recognize_entities_posology a pipeline with ner_posology. It will only extract medication entities.

More information and examples are available here: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.Pretrained_Clinical_Pipelines.ipynb.

Pretrained Named Entity Recognition and Relationship Extraction Models (English)

RE models:

re_temporal_events_clinical
re_temporal_events_enriched_clinical
re_human_phenotype_gene_clinical
re_drug_drug_interaction_clinical
re_chemprot_clinical

NER models:

ner_human_phenotype_gene_clinical
ner_human_phenotype_go_clinical
ner_chemprot_clinical

More information and examples here: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/10.Clinical_Relation_Extraction.ipynb

Pretrained Named Entity Recognition and Relationship Extraction Models (German)

The first time ever, we are releasing 3 licensed German models for healthcare and Legal domains.

  • German Clinical NER model for 19 clinical entities

  • German Legal NER model for 19 legal entities

  • German ICD-10GM

More information and examples here:

https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/14.German_Healthcare_Models.ipynb

https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/15.German_Legal_Model.ipynb

Other Pretrained Models

We now have Named Entity Disambiguation model out of the box.

Disambiguation models map words of interest, such as names of persons, locations and companies, from an input text document to corresponding unique entities in a target Knowledge Base (KB).

https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/12.Named_Entity_Disambiguation.ipynb

Due to ongoing requests about Clinical Entity Resolvers, we release a notebook to let you see how to train an entity resolver using an open source dataset based on Snomed.

https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/13.Snomed_Entity_Resolver_Model_Training.ipynb

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