Description
This pretrained pipeline is built on the top of re_date_clinical model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_date_clinical_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("This 73 y/o patient had CT on 1/12/95, with progressive memory and cognitive decline since 8/11/94.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_date_clinical_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("This 73 y/o patient had CT on 1/12/95, with progressive memory and cognitive decline since 8/11/94.")
import nlu
nlu.load("en.relation.date_clinical.pipeline").predict("""This 73 y/o patient had CT on 1/12/95, with progressive memory and cognitive decline since 8/11/94.""")
Results
| | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence |
|---|-----------|---------|---------------|-------------|------------------------------------------|---------|-------------|-------------|---------|------------|
| 0 | 1 | Test | 24 | 25 | CT | Date | 31 | 37 | 1/12/95 | 1.0 |
| 1 | 1 | Symptom | 45 | 84 | progressive memory and cognitive decline | Date | 92 | 98 | 8/11/94 | 1.0 |
Model Information
| Model Name: | re_date_clinical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- PerceptronModel
- DependencyParserModel
- RelationExtractionModel