Description
This pipeline is designed to extract radiology-related clinical/medical entities. In this pipeline, 3 NER models are used to extract the clinical entity labels.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("explain_clinical_doc_radiology_light", "en", "clinical/models")
result = ner_pipeline.annotate("""A 65-year-old woman had a history of debulking surgery, bilateral oophorectomy with omentectomy,
total anterior hysterectomy with radical pelvic lymph nodes dissection due to ovarian carcinoma (mucinous-type carcinoma, stage Ic) 1 year ago.
Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder.
This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow.
This may represent benign fibrous tissue or a lipoma.
""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("explain_clinical_doc_radiology_light", "en", "clinical/models")
val result = ner_pipeline.annotate("""A 65-year-old woman had a history of debulking surgery, bilateral oophorectomy with omentectomy,
total anterior hysterectomy with radical pelvic lymph nodes dissection due to ovarian carcinoma (mucinous-type carcinoma, stage Ic) 1 year ago.
Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder.
This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow.
This may represent benign fibrous tissue or a lipoma.
""")
Results
| | chunks | begin | end | entities |
|---:|:-----------------------------------------|--------:|------:|:--------------------------|
| 0 | woman | 14 | 18 | Gender |
| 1 | debulking surgery | 37 | 53 | Procedure |
| 2 | bilateral oophorectomy | 56 | 77 | Procedure |
| 3 | omentectomy | 84 | 94 | Procedure |
| 4 | total anterior hysterectomy | 98 | 124 | Procedure |
| 5 | radical pelvic lymph nodes dissection | 131 | 167 | Procedure |
| 6 | ovarian | 176 | 182 | BodyPart |
| 7 | carcinoma | 184 | 192 | Disease_Syndrome_Disorder |
| 8 | mucinous-type carcinoma | 195 | 217 | Disease_Syndrome_Disorder |
| 9 | stage Ic | 220 | 227 | Disease_Syndrome_Disorder |
| 10 | Bilateral | 243 | 251 | Direction |
| 11 | breast ultrasound | 253 | 269 | Imaging_Test |
| 12 | ovoid mass | 321 | 330 | ImagingFindings |
| 13 | 0.5 x 0.5 x 0.4 cm | 356 | 373 | Measurements |
| 14 | anteromedial aspect of the left shoulder | 406 | 445 | BodyPart |
| 15 | mass | 455 | 458 | ImagingFindings |
| 16 | isoechoic echotexture | 473 | 493 | ImagingFindings |
| 17 | adjacent muscle | 502 | 516 | BodyPart |
| 18 | internal color flow | 539 | 557 | ImagingFindings |
| 19 | benign fibrous tissue | 581 | 601 | ImagingFindings |
| 20 | lipoma | 608 | 613 | Disease_Syndrome_Disorder |
Model Information
| Model Name: | explain_clinical_doc_radiology_light |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.8 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel