Pipeline to Resolve CVX Codes

Description

This pretrained pipeline maps entities with their corresponding CVX codes. You’ll just feed your text and it will return the corresponding CVX codes.

Predicted Entities

Vaccine

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How to use

from sparknlp.pretrained import PretrainedPipeline

resolver_pipeline = PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")

text= "The patient has a history of influenza vaccine, tetanus and DTaP"

result = resolver_pipeline.fullAnnotate(text)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val resolver_pipeline = new PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")

val result = resolver_pipeline.fullAnnotate("The patient has a history of influenza vaccine, tetanus and DTaP")

import nlu
nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of influenza vaccine, tetanus and DTaP""")

Results

+-----------------+---------+--------+
|chunk            |ner_chunk|cvx_code|
+-----------------+---------+--------+
|influenza vaccine|Vaccine  |160     |
|tetanus          |Vaccine  |35      |
|DTaP             |Vaccine  |20      |
+-----------------+---------+--------+

Model Information

Included Models

• DocumentAssembler
• SentenceDetector
• TokenizerModel
• WordEmbeddingsModel
• MedicalNerModel
• NerConverterInternalModel
• ChunkMapperModel
• ChunkMapperFilterer
• Chunk2Doc
• BertSentenceEmbeddings
• SentenceEntityResolverModel
• ResolverMerger