Pipeline to Detect Diseases in Medical Text

Description

This pretrained pipeline is built on the top of bert_token_classifier_ner_bc5cdr_disease model.

Predicted Entities

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How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")

text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''

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

val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")

val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."

val result = pipeline.fullAnnotate(text)

Results

|    | ner_chunk             |   begin |   end | ner_label   |   confidence |
|---:|:----------------------|--------:|------:|:------------|-------------:|
|  0 | interstitial cystitis |      61 |    81 | DISEASE     |     0.999746 |
|  1 | mastocytosis          |     129 |   140 | DISEASE     |     0.999132 |
|  2 | cystitis              |     209 |   216 | DISEASE     |     0.999912 |
|  3 | prostate cancer       |     355 |   369 | DISEASE     |     0.999781 |

Model Information

Model Name: bert_token_classifier_ner_bc5cdr_disease_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