Description
This pretrained pipeline is built on the top of ner_oncology_biomarker_healthcare model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
result = pipeline.fullAnnotate(text)
Results
| | chunks | begin | end | entities | confidence |
|---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
| 0 | negative | 69 | 76 | Biomarker_Result | 1 |
| 1 | CK7 | 82 | 84 | Biomarker | 1 |
| 2 | synaptophysin | 87 | 99 | Biomarker | 1 |
| 3 | Syn | 102 | 104 | Biomarker | 0.9999 |
| 4 | chromogranin A | 108 | 121 | Biomarker | 0.99855 |
| 5 | CgA | 124 | 126 | Biomarker | 1 |
| 6 | Muc5AC | 130 | 135 | Biomarker | 0.9999 |
| 7 | human epidermal growth factor receptor-2 | 138 | 177 | Biomarker | 0.99994 |
| 8 | HER2 | 180 | 183 | Biomarker | 1 |
| 9 | Muc6 | 191 | 194 | Biomarker | 1 |
| 10 | positive | 197 | 204 | Biomarker_Result | 0.9997 |
| 11 | CK20 | 210 | 213 | Biomarker | 1 |
| 12 | Muc1 | 216 | 219 | Biomarker | 1 |
| 13 | Muc2 | 222 | 225 | Biomarker | 1 |
| 14 | E-cadherin | 228 | 237 | Biomarker | 0.9997 |
| 15 | p53 | 244 | 246 | Biomarker | 1 |
| 16 | Ki-67 index | 253 | 263 | Biomarker | 0.99865 |
| 17 | 87% | 275 | 277 | Biomarker_Result | 0.828 |
Model Information
| Model Name: | ner_oncology_biomarker_healthcare_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 533.1 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel