Description
This pretrained pipeline is built on the top of ner_oncology_biomarker model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
text = '''The 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_pipeline", "en", "clinical/models")
val text = "The 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_pipeline", "en", "clinical/models")
text = '''The 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
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
| 0 | negative | 70 | 77 | Biomarker_Result | 0.9984 |
| 1 | CK7 | 83 | 85 | Biomarker | 1 |
| 2 | synaptophysin | 88 | 100 | Biomarker | 0.9995 |
| 3 | Syn | 103 | 105 | Biomarker | 0.9979 |
| 4 | chromogranin A | 109 | 122 | Biomarker | 0.9692 |
| 5 | CgA | 125 | 127 | Biomarker | 0.9994 |
| 6 | Muc5AC | 131 | 136 | Biomarker | 0.9987 |
| 7 | human epidermal growth factor receptor-2 | 139 | 178 | Biomarker | 0.99876 |
| 8 | HER2 | 181 | 184 | Biomarker | 1 |
| 9 | Muc6 | 192 | 195 | Biomarker | 0.9999 |
| 10 | positive | 198 | 205 | Biomarker_Result | 0.9987 |
| 11 | CK20 | 211 | 214 | Biomarker | 1 |
| 12 | Muc1 | 217 | 220 | Biomarker | 0.9999 |
| 13 | Muc2 | 223 | 226 | Biomarker | 0.9999 |
| 14 | E-cadherin | 229 | 238 | Biomarker | 0.9999 |
| 15 | p53 | 245 | 247 | Biomarker | 1 |
| 16 | Ki-67 index | 254 | 264 | Biomarker | 0.99465 |
| 17 | 87% | 276 | 278 | Biomarker_Result | 0.9814 |
Model Information
| Model Name: | ner_oncology_biomarker_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