Description
This pipeline includes Named-Entity Recognition, Assertion Status and Relation Extraction models to extract information from oncology texts. This pipeline extracts diagnoses, treatments, tests, anatomical references and demographic entities.
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How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.""")
Results
******************** ner_oncology_wip results ********************
| chunk | ner_label |
|:---------------|:-----------------|
| left | Direction |
| mastectomy | Cancer_Surgery |
| left | Direction |
| breast cancer | Cancer_Dx |
| two months ago | Relative_Date |
| tumor | Tumor_Finding |
| positive | Biomarker_Result |
| ER | Biomarker |
| PR | Biomarker |
******************** ner_oncology_diagnosis_wip results ********************
| chunk | ner_label |
|:--------------|:--------------|
| breast cancer | Cancer_Dx |
| tumor | Tumor_Finding |
******************** ner_oncology_tnm_wip results ********************
| chunk | ner_label |
|:--------------|:------------|
| breast cancer | Cancer_Dx |
| tumor | Tumor |
******************** ner_oncology_therapy_wip results ********************
| chunk | ner_label |
|:-----------|:---------------|
| mastectomy | Cancer_Surgery |
******************** ner_oncology_test_wip results ********************
| chunk | ner_label |
|:---------|:-----------------|
| positive | Biomarker_Result |
| ER | Biomarker |
| PR | Biomarker |
******************** assertion_oncology_wip results ********************
| chunk | ner_label | assertion |
|:--------------|:---------------|:------------|
| mastectomy | Cancer_Surgery | Past |
| breast cancer | Cancer_Dx | Present |
| tumor | Tumor_Finding | Present |
| ER | Biomarker | Present |
| PR | Biomarker | Present |
******************** re_oncology_wip results ********************
| chunk1 | entity1 | chunk2 | entity2 | relation |
|:--------------|:-----------------|:---------------|:--------------|:--------------|
| mastectomy | Cancer_Surgery | two months ago | Relative_Date | is_related_to |
| breast cancer | Cancer_Dx | two months ago | Relative_Date | is_related_to |
| tumor | Tumor_Finding | ER | Biomarker | O |
| tumor | Tumor_Finding | PR | Biomarker | O |
| positive | Biomarker_Result | ER | Biomarker | is_related_to |
| positive | Biomarker_Result | PR | Biomarker | is_related_to |
******************** re_oncology_granular_wip results ********************
| chunk1 | entity1 | chunk2 | entity2 | relation |
|:--------------|:-----------------|:---------------|:--------------|:--------------|
| mastectomy | Cancer_Surgery | two months ago | Relative_Date | is_date_of |
| breast cancer | Cancer_Dx | two months ago | Relative_Date | is_date_of |
| tumor | Tumor_Finding | ER | Biomarker | O |
| tumor | Tumor_Finding | PR | Biomarker | O |
| positive | Biomarker_Result | ER | Biomarker | is_finding_of |
| positive | Biomarker_Result | PR | Biomarker | is_finding_of |
Model Information
| Model Name: | oncology_general_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- ChunkMergeModel
- ChunkMergeModel
- AssertionDLModel
- PerceptronModel
- DependencyParserModel
- RelationExtractionModel
- RelationExtractionModel