Description
This pretrained pipeline is built on the top of ner_eu_clinical_condition model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
text = "
Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
"
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
val text = "
Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
"
val result = pipeline.fullAnnotate(text)
Results
| | chunks | begin | end | entities | confidence |
|---:|:------------------------|--------:|------:|:-------------------|-------------:|
| 0 | Hyperparathyroidism | 1 | 19 | clinical_condition | 0.9375 |
| 1 | weakness | 77 | 84 | clinical_condition | 0.9779 |
| 2 | generalized joint pains | 90 | 112 | clinical_condition | 0.717333 |
| 3 | epigastric pain | 151 | 165 | clinical_condition | 0.64985 |
| 4 | gastritis | 191 | 199 | clinical_condition | 0.9543 |
| 5 | fractures | 281 | 289 | clinical_condition | 0.9726 |
| 6 | anesthesia | 305 | 314 | clinical_condition | 0.991 |
| 7 | mandibular fracture | 330 | 348 | clinical_condition | 0.54925 |
Model Information
| Model Name: | ner_eu_clinical_condition_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel