Pipeline to Detect Chemicals in Medical Text

Description

This pretrained pipeline is built on the top of bert_token_classifier_ner_bc5cdr_chemicals model.

Predicted Entities

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How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")

text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''

result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")

val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."

val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")

text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''

result = pipeline.fullAnnotate(text)

Results

|    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
|---:|:--------------------------|--------:|------:|:------------|-------------:|
|  0 | amphetamine               |     128 |   138 | CHEM        |     0.999973 |
|  1 | metamphetamine            |     144 |   157 | CHEM        |     0.999972 |
|  2 | p-choloroaniline          |     226 |   241 | CHEM        |     0.588953 |
|  3 | chlorhexidine-digluconate |     274 |   298 | CHEM        |     0.999979 |
|  4 | kanamycin                 |     350 |   358 | CHEM        |     0.999978 |
|  5 | colistin                  |     362 |   369 | CHEM        |     0.999942 |
|  6 | povidone-iodine           |     375 |   389 | CHEM        |     0.999977 |

Model Information

Model Name: bert_token_classifier_ner_bc5cdr_chemicals_pipeline
Type: pipeline
Compatibility: Healthcare NLP 4.4.4+
License: Licensed
Edition: Official
Language: en
Size: 404.8 MB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • MedicalBertForTokenClassifier
  • NerConverterInternalModel