Description
This pretrained pipeline is built on the top of rxnorm_umls_mapper model and maps RxNorm codes to corresponding UMLS codes
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
sample_text = """ [['amlodipine 5 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("rxnorm_umls_mapping", "en", "clinical/models")
sample_text = """ [['amlodipine 5 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("rxnorm_umls_mapping", "en", "clinical/models")
val sample_text = """ [['amlodipine 5 MG'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]"""
val result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))
Results
| chunk | rxnorm_code | umls_code |
| :------------------------- | ----------: | :-------- |
| amlodipine 5 MG | 197361 | C0687883 |
| magnesium hydroxide 100 MG | 337012 | C1134402 |
| metformin 1000 MG | 316255 | C0987664 |
| dilaudid | 224913 | C0728755 |
Model Information
| Model Name: | rxnorm_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.5 GB |
Included Models
- DocumentAssembler
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- Resolution2Chunk
- ChunkMapperModel