Description
This pretrained pipeline is built on the top of bert_token_classifier_drug_development_trials model.
Predicted Entities
ADE, Confidence_Interval, Confidence_Range, Confidence_level, DATE, Duration, End_Point, Follow_Up, Hazard_Ratio, P_Value, Patient_Count, Patient_Group, Trial_Group, Value
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
text = '''In June 2003, the median overall survival with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response ( CR ) , partial response ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with topotecan, and 11, 61, 66 and 32 patients, respectively, without topotecan.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
val text = "In June 2003, the median overall survival with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response ( CR ) , partial response ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with topotecan, and 11, 61, 66 and 32 patients, respectively, without topotecan."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In June 2003, the median overall survival with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response ( CR ) , partial response ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with topotecan, and 11, 61, 66 and 32 patients, respectively, without topotecan.""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:------------------------|--------:|------:|:--------------|-------------:|
| 0 | June 2003 | 3 | 11 | DATE | 0.996034 |
| 1 | median | 18 | 23 | Duration | 0.999535 |
| 2 | overall survival | 25 | 40 | End_Point | 0.996754 |
| 3 | without topotecan | 52 | 68 | Trial_Group | 0.976542 |
| 4 | 4.0 | 75 | 77 | Value | 0.998101 |
| 5 | 3.6 months | 83 | 92 | Value | 0.998159 |
| 6 | complete response ( CR | 118 | 140 | End_Point | 0.998629 |
| 7 | partial response ( PR | 146 | 167 | End_Point | 0.998672 |
| 8 | stable disease | 173 | 186 | End_Point | 0.996891 |
| 9 | progressive disease | 192 | 210 | End_Point | 0.997602 |
| 10 | 23 | 229 | 230 | Patient_Count | 0.998463 |
| 11 | 63 | 233 | 234 | Patient_Count | 0.996301 |
| 12 | 55 | 237 | 238 | Patient_Count | 0.996667 |
| 13 | 33 patients | 244 | 254 | Patient_Count | 0.995486 |
| 14 | topotecan | 277 | 285 | Trial_Group | 0.999624 |
| 15 | 11 | 293 | 294 | Patient_Count | 0.998747 |
| 16 | 61 | 297 | 298 | Patient_Count | 0.998314 |
| 17 | 66 | 301 | 302 | Patient_Count | 0.998066 |
| 18 | 32 patients | 308 | 318 | Patient_Count | 0.996285 |
| 19 | without topotecan | 335 | 351 | Trial_Group | 0.971218 |
Model Information
| Model Name: | bert_token_classifier_drug_development_trials_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 405.0 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverterInternalModel