Description
This pretrained model maps CPT codes to corresponding UMLS codes.
Predicted Entities
umls_code
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('doc')
chunk_assembler = Doc2Chunk()\
.setInputCols(['doc'])\
.setOutputCol('cpt_code')
mapperModel = ChunkMapperModel.load("cpt_umls_mapper")\
.setInputCols(["cpt_code"])\
.setOutputCol("mappings")\
mapper_pipeline = Pipeline(stages=[
document_assembler,
chunk_assembler,
mapperModel
])
data = spark.createDataFrame([["2016F"],["48155"],["64823"],["4500F"]]).toDF("text")
mapper_model = mapper_pipeline.fit(data)
result= mapper_model.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("doc")
val chunk_assembler = new Doc2Chunk()
.setInputCols(Array("doc"))
.setOutputCol("cpt_code")
val mapperModel = ChunkMapperModel.load("cpt_umls_mapper")
.setInputCols(Array("cpt_code"))
.setOutputCol("mappings")
val mapper_pipeline = new Pipeline().setStages(Array(
document_assembler,
chunk_assembler,
mapperModel ))
val data = Seq("2016F", "48155", "64823", "4500F").toDF("text")
val mapper_model = mapper_pipeline.fit(data)
result= mapper_model.transform(data)
Results
+--------+---------+
|cpt_code|umls_code|
+--------+---------+
|2016F |C3248275 |
|48155 |C0040511 |
|64823 |C0973430 |
|4500F |C3248301 |
+--------+---------+
Model Information
| Model Name: | cpt_umls_mapper |
| Compatibility: | Healthcare NLP 5.2.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 319.8 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.