Description
This pretrained pipeline is built on the top of ner_oncology model.
Predicted Entities
Available as Private API Endpoint
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
| 0 | left | 31 | 34 | Direction | 0.9913 |
| 1 | mastectomy | 36 | 45 | Cancer_Surgery | 0.952 |
| 2 | axillary lymph node dissection | 54 | 83 | Cancer_Surgery | 0.744525 |
| 3 | left | 91 | 94 | Direction | 0.9966 |
| 4 | breast cancer | 96 | 108 | Cancer_Dx | 0.9272 |
| 5 | twenty years ago | 110 | 125 | Relative_Date | 0.857067 |
| 6 | tumor | 132 | 136 | Tumor_Finding | 0.9959 |
| 7 | positive | 142 | 149 | Biomarker_Result | 0.9958 |
| 8 | ER | 155 | 156 | Biomarker | 0.9952 |
| 9 | PR | 162 | 163 | Biomarker | 0.9709 |
| 10 | radiotherapy | 183 | 194 | Radiotherapy | 0.9997 |
| 11 | breast | 229 | 234 | Site_Breast | 0.8288 |
| 12 | cancer | 241 | 246 | Cancer_Dx | 0.9949 |
| 13 | recurred | 248 | 255 | Response_To_Treatment | 0.9849 |
| 14 | right | 262 | 266 | Direction | 0.9993 |
| 15 | lung | 268 | 271 | Site_Lung | 0.9982 |
| 16 | metastasis | 273 | 282 | Metastasis | 0.9999 |
| 17 | 13 years later | 284 | 297 | Relative_Date | 0.791433 |
| 18 | adriamycin | 346 | 355 | Chemotherapy | 0.9999 |
| 19 | 60 mg/m2 | 358 | 365 | Dosage | 0.91785 |
| 20 | cyclophosphamide | 372 | 387 | Chemotherapy | 0.9999 |
| 21 | 600 mg/m2 | 390 | 398 | Dosage | 0.9647 |
| 22 | six courses | 406 | 416 | Cycle_Count | 0.6798 |
| 23 | first line | 422 | 431 | Line_Of_Therapy | 0.9792 |
Model Information
| Model Name: | ner_oncology_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