Description
A pipeline with ner_clinical, assertion_dl, re_clinical and ner_posology. It will extract clinical and medication entities, assign assertion status and find relationships between clinical entities.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
Results
| | chunks | ner_clinical | assertion | posology_chunk | ner_posology | relations |
|---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
| 0 | gestational diabetes mellitus | PROBLEM | present | metformin | Drug | TrAP |
| 1 | metformin | TREATMENT | present | 1000 mg | Strength | TrCP |
| 2 | polyuria | PROBLEM | present | two times a day | Frequency | TrCP |
| 3 | polydipsia | PROBLEM | present | 40 units | Dosage | TrWP |
| 4 | poor appetite | PROBLEM | present | insulin glargine | Drug | TrCP |
| 5 | vomiting | PROBLEM | present | at night | Frequency | TrAP |
| 6 | insulin glargine | TREATMENT | present | 12 units | Dosage | TrAP |
Model Information
| Model Name: | explain_clinical_doc_carp |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.8 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- DependencyParserModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- AssertionDLModel
- RelationExtractionModel