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.

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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