Description
This pipeline is designed to extract all entities mappable to HCC codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_hcc_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""The patient's medical record indicates a diagnosis of Diabetes and Chronic Obstructive Pulmonary Disease, requiring comprehensive care and management.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_hcc_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""The patient's medical record indicates a diagnosis of Diabetes and Chronic Obstructive Pulmonary Disease, requiring comprehensive care and management.""")
Results
| | chunks | begin | end | entities |
|---:|:--------------------------------------|--------:|------:|:-----------|
| 0 | Diabetes | 55 | 62 | PROBLEM |
| 1 | Chronic Obstructive Pulmonary Disease | 68 | 104 | PROBLEM |
Model Information
| Model Name: | ner_hcc_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel