Description
This pretrained pipeline is built on the top of clinical_notes_qa_large_onnx model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
qa_pipeline = PretrainedPipeline("clinical_notes_qa_large_onnx_pipeline", "en", "clinical/models")
context = """his is a 14-month-old with history of chronic recurrent episodes of otitis media, totalling 6 bouts, requiring antibiotics since birth. There is also associated chronic nasal congestion. There had been no bouts of spontaneous tympanic membrane perforation, but there had been elevations of temperature up to 102 during the acute infection. He is being admitted at this time for myringotomy and tube insertion under general facemask anesthesia."""
question = """How many bouts of otitis media has the patient experienced?"""
result = qa_pipeline.fullAnnotate([question], [context])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val qa_pipeline = PretrainedPipeline("clinical_notes_qa_large_onnx_pipeline", "en", "clinical/models")
val context = """his is a 14-month-old with history of chronic recurrent episodes of otitis media, totalling 6 bouts, requiring antibiotics since birth. There is also associated chronic nasal congestion. There had been no bouts of spontaneous tympanic membrane perforation, but there had been elevations of temperature up to 102 during the acute infection. He is being admitted at this time for myringotomy and tube insertion under general facemask anesthesia."""
val question = """How many bouts of otitis media has the patient experienced?"""
val result = qa_pipeline.fullAnnotate([question], [context])
Results
The patient has experienced 6 bouts of otitis media.
Model Information
| Model Name: | clinical_notes_qa_large_onnx_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.2.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.1 GB |
Included Models
- MultiDocumentAssembler
- MedicalQuestionAnswering