Description
This pretrained model maps SNOMED codes to corresponding ICDO codes under the Unified Medical Language System (UMLS).
Predicted Entities
icdo_code
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")
snomed_resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models") \
.setInputCols(["ner_chunk", "sbert_embeddings"]) \
.setOutputCol("snomed_code")\
.setDistanceFunction("EUCLIDEAN")
chunkerMapper = ChunkMapperModel\
.pretrained("snomed_icdo_mapper", "en", "clinical/models")\
.setInputCols(["snomed_code"])\
.setOutputCol("icdo_mappings")\
.setRels(["icdo_code"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
snomed_resolver,
chunkerMapper
])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
light_pipeline= LightPipeline(model)
result = light_pipeline.fullAnnotate("Structure of tendon of gluteus minimus")
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
.setInputCols("ner_chunk")
.setOutputCol("sbert_embeddings")
val snomed_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("snomed_code")
.setDistanceFunction("EUCLIDEAN")
val chunkerMapper = ChunkMapperModel
.pretrained("snomed_icdo_mapper", "en", "clinical/models")
.setInputCols("snomed_code")
.setOutputCol("icdo_mappings")
.setRels(Array("icdo_code"))
val pipeline = new Pipeline(stages = Array(
documentAssembler,
sbert_embedder,
snomed_resolver,
chunkerMapper
))
val data = Seq("Structure of tendon of gluteus minimus").toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.snomed_to_icdo").predict("""Structure of tendon of gluteus minimus""")
Results
| | ner_chunk | snomed_code | icdo_mappings |
|---:|:---------------------------------------|:------------|----------------:|
| 0 | Structure of tendon of gluteus minimus | 128501000 | C49.5 |
Model Information
| Model Name: | snomed_icdo_mapper |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [snomed_code] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 203.5 KB |