Description
A pretrained pipeline to detect abbreviations and acronyms of medical regulatory activities as well as map them with their definitions and categories.
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