Description
This pretrained pipeline is built on the top of ner_events_clinical model.
Predicted Entities
CLINICAL_DEPT, DATE, DURATION, EVIDENTIAL, FREQUENCY, OCCURRENCE, PROBLEM, TEST, TIME, TREATMENT
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
text = '''The patient presented to the emergency room last evening.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
val text = "The patient presented to the emergency room last evening."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-------------------|--------:|------:|:--------------|-------------:|
| 0 | presented | 12 | 20 | OCCURRENCE | 0.7132 |
| 1 | the emergency room | 25 | 42 | CLINICAL_DEPT | 0.723267 |
| 2 | last evening | 44 | 55 | DATE | 0.90555 |
Model Information
| Model Name: | ner_events_clinical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel