Description
This pretrained pipeline is built on the top of umls_rxnorm_mapper model and maps UMLS codes to corresponding RxNorm codes
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("umls_rxnorm_mapping", "en", "clinical/models")
sample_text = """ [['Hydrogen peroxide 30 mg'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]"""
result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
from johnsnowlabs import nlp, medical
pipeline = nlp.PretrainedPipeline("umls_rxnorm_mapping", "en", "clinical/models")
sample_text = """ [['Hydrogen peroxide 30 mg'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]"""
result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("umls_rxnorm_mapping", "en", "clinical/models")
val sample_text = """ [['Hydrogen peroxide 30 mg'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]"""
val result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
Results
| chunk | umls_code | rxnorm_code |
| :------------------------- | :-------- | ----------: |
| Hydrogen peroxide 30 mg | C1126248 | 330565 |
| magnesium hydroxide 100 MG | C1134402 | 337012 |
| metformin 1000 MG | C0987664 | 316255 |
| dilaudid | C0728755 | 224913 |
Model Information
| Model Name: | umls_rxnorm_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.4 GB |
Included Models
- DocumentAssembler
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- Resolution2Chunk
- ChunkMapperModel