Description
This pipeline is designed to extract all entities mappable to ICD-10-PCS codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_icd10pcs_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""Given the severity of her abdominal examination and her persistence of her symptoms,
it is detected that need for laparoscopic appendectomy and possible jejunectomy
as well as pyeloplasty. We recommend performing a mediastinoscopy""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_icd10pcs_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""Given the severity of her abdominal examination and her persistence of her symptoms,
it is detected that need for laparoscopic appendectomy and possible jejunectomy
as well as pyeloplasty. We recommend performing a mediastinoscopy""")
Results
| | chunks | begin | end | entities |
|---:|:--------------------------|--------:|------:|:-----------|
| 0 | laparoscopic appendectomy | 127 | 151 | Procedure |
| 1 | jejunectomy | 166 | 176 | Procedure |
| 2 | pyeloplasty | 201 | 211 | Procedure |
| 3 | mediastinoscopy | 240 | 254 | Procedure |
Model Information
| Model Name: | ner_icd10pcs_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
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel