Description
This pretrained pipeline is built on the top of ner_abbreviation_clinical model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
text = '''Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
val text = "Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:------------|-------------:|
| 0 | CBC | 126 | 128 | ABBR | 1 |
| 1 | AB | 159 | 160 | ABBR | 1 |
| 2 | VDRL | 189 | 192 | ABBR | 1 |
| 3 | HIV | 247 | 249 | ABBR | 1 |
Model Information
| Model Name: | ner_abbreviation_clinical_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