Description
This pretrained pipeline is built on the top of ner_jsl_slim model.
How to use
pipeline = PretrainedPipeline("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("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.med_ner.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 | entity |
|---:|:-----------------|:-------------|
| 0 | HISTORY: | Header |
| 1 | 30-year-old | Age |
| 2 | female | Demographics |
| 3 | mammography | Test |
| 4 | soft tissue lump | Symptom |
| 5 | shoulder | Body_Part |
| 6 | breast cancer | Oncological |
| 7 | her mother | Demographics |
| 8 | age 58 | Age |
| 9 | breast cancer | Oncological |
Model Information
| Model Name: | ner_jsl_slim_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