Description
A pretrained pipeline with ner_clinical_events, assertion_dl and re_temporal_events_clinical trained with embeddings_healthcare_100d. It will extract clinical entities, assign assertion status and find temporal relationships between clinical entities.
Live Demo Open in Colab Copy S3 URI
How to use
era_pipeline = PretrainedPipeline('explain_clinical_doc_era', 'en', 'clinical/models')
annotations = era_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]
annotations.keys()
val era_pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
val result = era_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)
import nlu
nlu.load("en.explain_doc.era").predict("""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. """)
Results
The output is a dictionary with the following keys: 'sentences', 'clinical_ner_tags', 'clinical_ner_chunks_re', 'document', 'clinical_ner_chunks', 'assertion', 'clinical_relations', 'tokens', 'embeddings', 'pos_tags', 'dependencies'. Here is the result of clinical_ner_chunks :
| # | chunks | begin | end | entities |
|----|-------------------------------|-------|-----|---------------|
| 0 | admitted | 7 | 14 | OCCURRENCE |
| 1 | The John Hopkins Hospital | 19 | 43 | CLINICAL_DEPT |
| 2 | 2 days ago | 45 | 54 | DATE |
| 3 | gestational diabetes mellitus | 74 | 102 | PROBLEM |
| 4 | diagnosed | 104 | 112 | OCCURRENCE |
| 5 | denied | 119 | 124 | EVIDENTIAL |
| 6 | pain | 126 | 129 | PROBLEM |
| 7 | any headache | 135 | 146 | PROBLEM |
| 8 | seen | 157 | 160 | OCCURRENCE |
| 9 | the endocrinology service | 165 | 189 | CLINICAL_DEPT |
| 10 | discharged | 203 | 212 | OCCURRENCE |
| 11 | 03/02/2018 | 217 | 226 | DATE |
| 12 | insulin glargine | 243 | 258 | TREATMENT |
| 13 | insulin lispro | 274 | 287 | TREATMENT |
| 14 | metformin | 294 | 302 | TREATMENT |
| 15 | two times a day | 312 | 326 | FREQUENCY |
| 16 | close follow-up | 337 | 351 | OCCURRENCE |
| 17 | endocrinology | 358 | 370 | CLINICAL_DEPT |
| 18 | discharge | 377 | 385 | OCCURRENCE |
Model Information
| Model Name: | explain_clinical_doc_era |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 2.6.0 + |
| License: | Licensed |
| Edition: | Official |
| Language: | [en] |
Included Models
ner_clinical_eventsassertion_dlre_temporal_events_clinical