Description
This pretrained pipeline is built on the top of ner_oncology_diagnosis model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:------------------|-------------:|
| 0 | tumor | 44 | 48 | Tumor_Finding | 0.9958 |
| 1 | adenopathies | 73 | 84 | Adenopathy | 0.6287 |
| 2 | invasive | 110 | 117 | Histological_Type | 0.9965 |
| 3 | ductal | 119 | 124 | Histological_Type | 0.9996 |
| 4 | carcinoma | 126 | 134 | Cancer_Dx | 0.9988 |
| 5 | metastasis | 181 | 190 | Metastasis | 0.9996 |
Model Information
| Model Name: | ner_oncology_diagnosis_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