Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_clinical_trials_abstracts model.
Predicted Entities
Age, AllocationRatio, Author, BioAndMedicalUnit, CTAnalysisApproach, CTDesign, Confidence, Country, DisorderOrSyndrome, DoseValue, Drug, DrugTime, Duration, Journal, NumberPatients, PMID, PValue, PercentagePatients, PublicationYear, TimePoint, Value
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-----------------|--------:|------:|:-------------------|-------------:|
| 0 | open-label | 5 | 14 | CTDesign | 0.742075 |
| 1 | parallel-group | 17 | 30 | CTDesign | 0.725741 |
| 2 | two-arm | 33 | 39 | CTDesign | 0.427547 |
| 3 | insulin glargine | 105 | 120 | Drug | 0.985063 |
| 4 | GLA | 123 | 125 | Drug | 0.96917 |
| 5 | NPH insulin | 132 | 142 | Drug | 0.762519 |
| 6 | metformin | 155 | 163 | Drug | 0.996344 |
| 7 | 28 | 175 | 176 | NumberPatients | 0.968501 |
| 8 | type 2 diabetes | 192 | 206 | DisorderOrSyndrome | 0.979685 |
| 9 | 61.5 | 235 | 238 | Age | 0.610416 |
| 10 | kg/m(2 | 273 | 278 | BioAndMedicalUnit | 0.974807 |
| 11 | metformin | 295 | 303 | Drug | 0.99696 |
| 12 | sulfonylurea | 309 | 320 | Drug | 0.996722 |
| 13 | randomized | 327 | 336 | CTDesign | 0.990632 |
| 14 | once-daily | 345 | 354 | DrugTime | 0.472084 |
| 15 | GLA | 356 | 358 | Drug | 0.972978 |
| 16 | NPH | 363 | 365 | Drug | 0.989424 |
| 17 | bedtime | 370 | 376 | DrugTime | 0.936016 |
Model Information
| Model Name: | bert_token_classifier_ner_clinical_trials_abstracts_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.9 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverterInternalModel