Description
A pretrained pipeline with ner_clinical and assertion_dl. It will extract clinical entities and assign assertion status for them.
How to use
pipeline = PretrainedPipeline("clinical_ner_assertion","en","clinical/models")
result = pipe_model.fullAnnotate("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache.She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge.""")[0]
result.keys()
val pipeline = new PretrainedPipeline("clinical_ner_assertion","en","clinical/models")
val result = pipeline.fullAnnotate("She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache.She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge.")(0)
Results
chunks entities assertion
0 gestational diabetes mellitus PROBLEM present
1 pain PROBLEM absent
2 headache PROBLEM absent
3 insulin glargine TREATMENT present
4 insulin lispro TREATMENT present
5 metformin TREATMENT present
Model Information
| Name: | clinical_ner_assertion |
| Type: | PipelineModel |
| Compatibility: | Spark NLP 2.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- ner_clinical
- assertion_dl
PREVIOUSClinical Deidentification