Description
A pretrained pipeline to detect abbreviations and acronyms of medical regulatory activities as well as map them with their definitions and categories.
Predicted Entities
ABBR
How to use
from sparknlp.pretrained import PretrainedPipeline
abbr_pipeline = PretrainedPipeline("abbreviation_pipeline", "en", "clinical/models")
result = abbr_pipeline.fullAnnotate("""Gravid with estimated fetal weight of 6-6/12 pounds.
LABORATORY DATA: Laboratory tests include a CBC which is normal.
VDRL: Nonreactive
HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val abbr_pipeline = new PretrainedPipeline("abbreviation_pipeline", "en", "clinical/models")
val result = abbr_pipeline.fullAnnotate("""Gravid with estimated fetal weight of 6-6/12 pounds.
LABORATORY DATA: Laboratory tests include a CBC which is normal.
VDRL: Nonreactive
HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
Results
+-----+------+-----------------+----------------------------------------+
|chunk|entity|category_mappings| definition_mappings|
+-----+------+-----------------+----------------------------------------+
| CBC| ABBR| general|complete blood count ...|
| VDRL| ABBR| clinical_dept| Venereal Disease Research Laboratories|
| HIV| ABBR|medical_condition| Human immunodeficiency virus|
+-----+------+-----------------+----------------------------------------+
Model Information
| Model Name: | abbreviation_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.0.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- ChunkMapperModel
- ChunkMapperModel