Description
This pretrained pipeline is built on the top of ner_posology_healthcare model.
Predicted Entities
Drug, Strength, Route, Frequency, Dosage, Form, Duration
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
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 chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
val 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 chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""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 chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:--------------|--------:|------:|:------------|-------------:|
| 0 | Aspirin | 267 | 273 | Drug | 0.9983 |
| 1 | 81 milligrams | 275 | 287 | Strength | 0.9921 |
| 2 | QDay | 289 | 292 | Frequency | 0.995 |
| 3 | insulin | 295 | 301 | Drug | 0.9469 |
| 4 | 50 units | 303 | 310 | Dosage | 0.6343 |
| 5 | in a.m | 312 | 317 | Frequency | 0.71315 |
| 6 | HCTZ | 320 | 323 | Drug | 0.9789 |
| 7 | 50 mg | 325 | 329 | Strength | 0.96705 |
| 8 | QDay | 331 | 334 | Frequency | 0.9877 |
| 9 | Nitroglycerin | 337 | 349 | Drug | 0.9927 |
| 10 | 1/150 | 351 | 355 | Strength | 0.9565 |
| 11 | sublingually. | 357 | 369 | Route | 0.72065 |
Model Information
| Model Name: | ner_posology_healthcare_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 513.8 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel