Description
This pretrained pipeline is built on the top of ner_posology_large_biobert model.
Predicted Entities
DOSAGE, DRUG, DURATION, FORM, FREQUENCY, ROUTE, STRENGTH
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:--------------------|--------:|------:|:------------|-------------:|
| 0 | 1 | 27 | 27 | DOSAGE | 0.9998 |
| 1 | capsule | 29 | 35 | FORM | 0.9978 |
| 2 | Advil | 40 | 44 | DRUG | 0.9992 |
| 3 | 10 mg | 46 | 50 | STRENGTH | 0.8269 |
| 4 | for 5 days | 52 | 61 | DURATION | 0.978333 |
| 5 | magnesium hydroxide | 67 | 85 | DRUG | 0.9783 |
| 6 | 100mg/1ml | 87 | 95 | STRENGTH | 0.9336 |
| 7 | suspension | 97 | 106 | FORM | 0.9999 |
| 8 | PO | 108 | 109 | ROUTE | 0.9871 |
| 9 | 40 units | 179 | 186 | DOSAGE | 0.6543 |
| 10 | insulin glargine | 191 | 206 | DRUG | 0.97145 |
| 11 | at night | 208 | 215 | FREQUENCY | 0.83505 |
| 12 | 12 units | 218 | 225 | DOSAGE | 0.69795 |
| 13 | insulin lispro | 230 | 243 | DRUG | 0.89265 |
| 14 | with meals | 245 | 254 | FREQUENCY | 0.8772 |
| 15 | metformin | 261 | 269 | DRUG | 1 |
| 16 | 1000 mg | 271 | 277 | STRENGTH | 0.69955 |
| 17 | two times a day | 279 | 293 | FREQUENCY | 0.758125 |
Model Information
| Model Name: | ner_posology_large_biobert_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 422.1 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- BertEmbeddings
- MedicalNerModel
- NerConverterInternalModel