Description
This pretrained pipeline is built on the top of ner_oncology_response_to_treatment model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:----------------------|-------------:|
| 0 | recurrence | 70 | 79 | Response_To_Treatment | 0.9767 |
Model Information
| Model Name: | ner_oncology_response_to_treatment_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