Description
This pipeline, combines dosage, strength, form, and route into a single entity: Drug.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_drugs_large_v2_pipeline", "en", "clinical/models")
sample_text = """
The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'.
The patient is diabetic and has a prior history of coronary artery disease.
The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent.
He has been advised Aspirin 81 milligrams QDay.
Humulin N. insulin 50 units in a.m.
HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.
"""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
from johnsnowlabs import nlp, medical
pipeline = nlp.PretrainedPipeline("ner_drugs_large_v2_pipeline", "en", "clinical/models")
sample_text = """
The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'.
The patient is diabetic and has a prior history of coronary artery disease.
The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent.
He has been advised Aspirin 81 milligrams QDay.
Humulin N. insulin 50 units in a.m.
HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.
"""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("ner_drugs_large_v2_pipeline", "en", "clinical/models")
val sample_text = """
The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'.
The patient is diabetic and has a prior history of coronary artery disease.
The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent.
He has been advised Aspirin 81 milligrams QDay.
Humulin N. insulin 50 units in a.m.
HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.
"""
val result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
Results
| chunk | begin | end | ner_label |
| :------------ | ----: | --: | :-------- |
| Aspirin | 307 | 313 | DRUG |
| Humulin N | 335 | 343 | DRUG |
| insulin | 346 | 352 | DRUG |
| HCTZ | 371 | 374 | DRUG |
| Nitroglycerin | 388 | 400 | DRUG |
Model Information
| Model Name: | ner_drugs_large_v2_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.0 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel