Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_jsl_slim model.
How to use
pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
pipeline.annotate("HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.")
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
pipeline.annotate("HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.")
import nlu
nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.""")
Results
+----------------+------------+
|chunk |ner_label |
+----------------+------------+
|HISTORY: |Header |
|30-year-old |Age |
|female |Demographics|
|mammography |Test |
|soft tissue lump|Symptom |
|shoulder |Body_Part |
|breast cancer |Oncological |
|her mother |Demographics|
|age 58 |Age |
|breast cancer |Oncological |
+----------------+------------+
Model Information
| Model Name: | bert_token_classifier_ner_jsl_slim_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.8 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverter