Pipeline to find clinical events and find temporal relations (ERA)

Description

A pipeline with ner_clinical_events, assertion_dl and re_temporal_events_clinical. It will extract clinical entities, assign assertion status and find temporal relationships between clinical entities.

Predicted Entities

CLINICAL_DEPT, DATE, DURATION, EVIDENTIAL, FREQUENCY, OCCURRENCE, PROBLEM, TEST, TIME, TREATMENT

Download Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")

text = """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. """

result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")

val text = """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. """

val result = pipeline.fullAnnotate(text)
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


|    | relation   | entity1       |   entity1_begin |   entity1_end | chunk1                    | entity2       |   entity2_begin |   entity2_end | chunk2                        |   confidence |
|---:|:-----------|:--------------|----------------:|--------------:|:--------------------------|:--------------|----------------:|--------------:|:------------------------------|-------------:|
|  0 | AFTER      | OCCURRENCE    |               7 |            14 | admitted                  | CLINICAL_DEPT |              19 |            43 | The John Hopkins Hospital     |     0.963836 |
|  1 | BEFORE     | OCCURRENCE    |               7 |            14 | admitted                  | DATE          |              45 |            54 | 2 days ago                    |     0.587098 |
|  2 | BEFORE     | OCCURRENCE    |               7 |            14 | admitted                  | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.999991 |
|  3 | OVERLAP    | CLINICAL_DEPT |              19 |            43 | The John Hopkins Hospital | DATE          |              45 |            54 | 2 days ago                    |     0.996056 |
|  4 | BEFORE     | CLINICAL_DEPT |              19 |            43 | The John Hopkins Hospital | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.995216 |
|  5 | OVERLAP    | DATE          |              45 |            54 | 2 days ago                | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.996954 |
|  6 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             126 |           129 | pain                          |     1        |
|  7 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             135 |           146 | any headache                  |     1        |

Model Information

Model Name: explain_clinical_doc_era
Type: pipeline
Compatibility: Healthcare NLP 4.3.0+
License: Licensed
Edition: Official
Language: en
Size: 1.7 GB

Included Models

  • DocumentAssembler
  • SentenceDetector
  • TokenizerModel
  • PerceptronModel
  • DependencyParserModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverterInternalModel
  • RelationExtractionModel
  • AssertionDLModel