Description
This pretrained pipeline is built on the top of re_test_result_date model.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
import nlu
nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
Results
| index | relations | entity1 | chunk1 | entity2 | chunk2 |
|-------|--------------|--------------|---------------------|--------------|---------|
| 0 | O | TEST | chest X-ray | MEASUREMENTS | 93% |
| 1 | O | TEST | CT scan | MEASUREMENTS | 93% |
| 2 | is_result_of | TEST | SpO2 | MEASUREMENTS | 93% |
Model Information
| Model Name: | re_test_result_date_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- DependencyParserModel
- RelationExtractionModel