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  • John Snow Labs
  • Jun 17, 2023

Pipeline to Detect Living Species

  • en
  • ner
  • clinical
  • licensed

Description

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

Predicted Entities

Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline

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

text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''

result = pipeline.fullAnnotate(text)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

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

val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."

val result = pipeline.fullAnnotate(text)

from sparknlp.pretrained import PretrainedPipeline

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

text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''

result = pipeline.fullAnnotate(text)

Results

|    | ner_chunks              |   begin |   end | ner_label   |   confidence |
|---:|:------------------------|--------:|------:|:------------|-------------:|
|  0 | woman                   |      12 |    16 | HUMAN       |     0.9993   |
|  1 | bacterial               |     145 |   153 | SPECIES     |     0.9815   |
|  2 | Fusarium spp            |     337 |   348 | SPECIES     |     0.9644   |
|  3 | patient                 |     355 |   361 | HUMAN       |     0.9984   |
|  4 | species                 |     507 |   513 | SPECIES     |     0.8838   |
|  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.748667 |
|  6 | antifungals             |     792 |   802 | SPECIES     |     0.9847   |

Model Information

Model Name: ner_living_species_pipeline
Type: pipeline
Compatibility: Healthcare NLP 4.4.4+
License: Licensed
Edition: Official
Language: en
Size: 1.7 GB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverterInternalModel