Description
This pretrained pipeline is built on the top of snomed_umls_mapper model and maps SNOMED codes to corresponding UMLS codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
sample_text = """ [['acebutolol'], ['fluids']]"""
result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
from johnsnowlabs import nlp, medical
pipeline = nlp.PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
sample_text = """ [['acebutolol'], ['fluids']]"""
result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
val sample_text = """ [['acebutolol'], ['fluids']]"""
val result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
Results
| chunk | snomed_code | umls_code |
| :--------- | ----------: | :-------- |
| acebutolol | 68088000 | C0000946 |
| fluids | 118431008 | C1289919 |
Model Information
| Model Name: | snomed_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 692.6 MB |
Included Models
- DocumentAssembler
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- Resolution2Chunk
- ChunkMapperModel