Pipeline for Anatomic Therapeutic Chemical (ATC) Sentence Entity Resolver

Description

This advanced pipeline extracts DRUG entities from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding Anatomic Therapeutic Chemical (ATC) codes.

Predicted Entities

DRUG

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Available as Private API Endpoint

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How to use

from sparknlp.pretrained import PretrainedPipeline

ner_pipeline = PretrainedPipeline("atc_resolver_pipeline", "en", "clinical/models")

result = ner_pipeline.annotate("""She was immediately given hydrogen peroxide 30 mg and amoxicillin twice daily for 10 days to treat the infection on her leg. She has a history of taking magnesium hydroxide.""")

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val ner_pipeline = PretrainedPipeline("atc_resolver_pipeline", "en", "clinical/models")

val result = ner_pipeline.annotate("""She was immediately given hydrogen peroxide 30 mg and amoxicillin twice daily for 10 days to treat the infection on her leg. She has a history of taking magnesium hydroxide.""")

Results

|    | chunks              |   begin |   end | entities   | atc_code   | resolutions         |
|---:|:--------------------|--------:|------:|:-----------|:-----------|:--------------------|
|  0 | hydrogen peroxide   |      26 |    42 | DRUG       | A01AB02    | hydrogen peroxide   |
|  1 | amoxicillin         |      54 |    64 | DRUG       | J01CA04    | amoxicillin         |
|  2 | magnesium hydroxide |     153 |   171 | DRUG       | A02AA04    | magnesium hydroxide |

Model Information

Included Models

• DocumentAssembler
• SentenceDetectorDLModel
• TokenizerModel
• WordEmbeddingsModel
• MedicalNerModel
• NerConverterInternalModel
• Chunk2Doc
• BertSentenceEmbeddings
• SentenceEntityResolverModel