Description
This pretrained model maps UMLS codes to corresponding CPT codes.
Predicted Entities
cpt_code
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('doc')
chunkAssembler = Doc2Chunk()\
.setInputCols("doc")\
.setOutputCol("umls_code")\
chunkerMapper = ChunkMapperModel.load("umls_cpt_mapper")\
.setInputCols(["umls_code"])\
.setOutputCol("mappings")\
mapper_pipeline = Pipeline(stages=[
document_assembler,
chunkAssembler,
chunkerMapper
])
data = spark.createDataFrame([["C3248275"],["C3496535"],["C0973430"],["C3248301"]]).toDF("text")
mapper_model = mapper_pipeline.fit(data)
result= mapper_model.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("doc")
val chunkAssembler = new Doc2Chunk()
.setInputCols("doc")
.setOutputCol("umls_code")
val chunkerMapper = ChunkMapperModel.load("umls_cpt_mapper")
.setInputCols(Array("umls_code"))
.setOutputCol("mappings")
val mapper_pipeline = new Pipeline().setStages(Array(
document_assembler,
chunkAssembler,
chunkerMapper))
val data = Seq("C3248275", "C3496535", "C0973430", "C3248301").toDF("text")
val mapper_model = mapper_pipeline.fit(data)
result= mapper_model.transform(data)
Results
+---------+--------+
|umls_code|cpt_code|
+---------+--------+
|C3248275 |2016F |
|C3496535 |48155 |
|C0973430 |64823 |
|C3248301 |4500F |
+---------+--------+
Model Information
| Model Name: | umls_cpt_mapper |
| Compatibility: | Healthcare NLP 5.2.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 601.8 KB |
References
CPT resolver models are removed from the Models Hub due to license restrictions and can only be shared with the users who already have a valid CPT license. If you possess one and wish to use this model, kindly contact us at support@johnsnowlabs.com.