Description
This pretrained pipeline is built on the top of ner_oncology_posology model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------|--------:|------:|:---------------|-------------:|
| 0 | adriamycin | 46 | 55 | Cancer_Therapy | 1 |
| 1 | 60 mg/m2 | 58 | 65 | Dosage | 0.92005 |
| 2 | cyclophosphamide | 72 | 87 | Cancer_Therapy | 0.9999 |
| 3 | 600 mg/m2 | 90 | 98 | Dosage | 0.9229 |
| 4 | six courses | 106 | 116 | Cycle_Count | 0.494 |
| 5 | second cycle | 150 | 161 | Cycle_Number | 0.98675 |
| 6 | chemotherapy | 166 | 177 | Cancer_Therapy | 1 |
Model Information
| Model Name: | ner_oncology_posology_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