Description
This pretrained pipeline is built on the top of ner_healthcare model.
Predicted Entities
BIOLOGICAL_CHEMISTRY, BIOLOGICAL_PARAMETER, BODY_FLUID, BODY_PART, DEGREE, DIAGLAB_PROCEDURE, DOSING, LOCAL_SPECIFICATION, MEASUREMENT, MEDICAL_CONDITION, MEDICAL_DEVICE, MEDICAL_SPECIFICATION, MEDICATION, PERSON, PROCESS, STATE_OF_HEALTH, TIME_INFORMATION, TISSUE, TREATMENT
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG ."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:------------------------------|--------:|------:|:------------|-------------:|
| 0 | gestational diabetes mellitus | 39 | 67 | PROBLEM | 0.938233 |
| 1 | type two diabetes mellitus | 128 | 153 | PROBLEM | 0.762925 |
| 2 | HTG-induced pancreatitis | 186 | 209 | PROBLEM | 0.9742 |
| 3 | an acute hepatitis | 263 | 280 | PROBLEM | 0.915067 |
| 4 | obesity | 288 | 294 | PROBLEM | 0.9926 |
| 5 | a body mass index | 301 | 317 | TEST | 0.721175 |
| 6 | BMI | 321 | 323 | TEST | 0.4466 |
| 7 | polyuria | 380 | 387 | PROBLEM | 0.9987 |
| 8 | polydipsia | 391 | 400 | PROBLEM | 0.9993 |
| 9 | poor appetite | 404 | 416 | PROBLEM | 0.96315 |
| 10 | vomiting | 424 | 431 | PROBLEM | 0.9588 |
| 11 | amoxicillin | 511 | 521 | TREATMENT | 0.6453 |
| 12 | a respiratory tract infection | 527 | 555 | PROBLEM | 0.867 |
| 13 | metformin | 570 | 578 | TREATMENT | 0.9989 |
| 14 | glipizide | 582 | 590 | TREATMENT | 0.9997 |
| 15 | dapagliflozin | 598 | 610 | TREATMENT | 0.9996 |
| 16 | T2DM | 616 | 619 | TREATMENT | 0.9662 |
| 17 | atorvastatin | 625 | 636 | TREATMENT | 0.9993 |
| 18 | gemfibrozil | 642 | 652 | TREATMENT | 0.9997 |
| 19 | HTG | 658 | 660 | PROBLEM | 0.9927 |
Model Information
| Model Name: | ner_healthcare_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 513.7 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel