Description
This pretrained model maps RxNorm codes to corresponding UMLS codes.
Predicted Entities
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en","clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")\
.setInputCols(["sbert_embeddings"])\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
resolver2chunk = Resolution2Chunk()\
.setInputCols(["rxnorm_code"])\
.setOutputCol("rxnorm2chunk")
chunkerMapper = ChunkMapperModel.pretrained("rxnorm_umls_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm2chunk"])\
.setOutputCol("umls_mappings")\
.setRels(["umls_code"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver,
resolver2chunk,
chunkerMapper
])
data = spark.createDataFrame([['amlodipine 5 MG'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sbert_embeddings")
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val resolver2chunk = Resolution2Chunk()
.setInputCols(Array("rxnorm_code"))
.setOutputCol("rxnorm2chunk")
val chunkerMapper = ChunkMapperModel.pretrained("rxnorm_umls_mapper", "en", "clinical/models")
.setInputCols(Array("rxnorm_code"))
.setOutputCol("umls_mappings")
.setRels(Array("umls_code"))
val pipeline = Pipeline().setStages(Array(
documentAssembler,
sbert_embedder,
rxnorm_resolver,
resolver2chunk,
chunkerMapper)
val data = Seq(
("amlodipine 5 MG"),
("magnesium hydroxide 100 MG"),
("metformin 1000 MG"),
("dilaudid")
).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+--------------------------+-----------+---------+
|chunk |rxnorm_code|umls_code|
+--------------------------+-----------+---------+
|amlodipine 5 MG |329528 |C1124796 |
|magnesium hydroxide 100 MG|337012 |C1134402 |
|metformin 1000 MG |316255 |C0987664 |
|dilaudid |224913 |C0728755 |
+--------------------------+-----------+---------+
Model Information
| Model Name: | rxnorm_umls_mapper |
| Compatibility: | Healthcare NLP 5.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 3.0 MB |