Description
This pretrained pipeline is built on the top of ner_events_clinical model.
How to use
pipeline = PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
pipeline.annotate("The patient presented to the emergency room last evening")
val pipeline = new PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
pipeline.annotate("The patient presented to the emergency room last evening")
import nlu
nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented to the emergency room last evening""")
Results
+------------------+-------------+
|chunk |ner_label |
+------------------+-------------+
|presented |OCCURRENCE |
|the emergency room|CLINICAL_DEPT|
|last evening |DATE |
+------------------+-------------+
Model Information
| Model Name: | ner_events_clinical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter