Description
This pipeline, extracts medical procedure entities in clinical text. It recognizes procedures including colonoscopy, biopsy, MRI, CT scan, echocardiogram, endoscopy, blood transfusion, and more.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("procedure_matcher_pipeline", "en", "clinical/models")
sample_text = """ The patient presented with gastrointestinal symptoms. A colonoscopy was performed which revealed a suspicious lesion, and a biopsy was obtained for pathological examination."""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
from johnsnowlabs import nlp, medical
pipeline = nlp.PretrainedPipeline("procedure_matcher_pipeline", "en", "clinical/models")
sample_text = """ The patient presented with gastrointestinal symptoms. A colonoscopy was performed which revealed a suspicious lesion, and a biopsy was obtained for pathological examination."""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("procedure_matcher_pipeline", "en", "clinical/models")
val sample_text = """ The patient presented with gastrointestinal symptoms. A colonoscopy was performed which revealed a suspicious lesion, and a biopsy was obtained for pathological examination."""
val result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
Results
| chunk | begin | end | label |
| :---------- | ----: | --: | :-------- |
| colonoscopy | 56 | 66 | PROCEDURE |
| biopsy | 124 | 129 | PROCEDURE |
Model Information
| Model Name: | procedure_matcher_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 965.8 KB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- TextMatcherInternalModel
- ChunkConverter