Description
This pretrained model maps ICDO codes to corresponding SNOMED 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")
icdo_resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_icdo_augmented", "en", "clinical/models")\
.setInputCols(["ner_chunk", "sbert_embeddings"]) \
.setOutputCol("icdo_code")\
.setDistanceFunction("EUCLIDEAN")
chunkerMapper = ChunkMapperModel\
.pretrained("icdo_snomed_mapper", "en", "clinical/models")\
.setInputCols(["icdo_code"])\
.setOutputCol("snomed_mappings")\
.setRels(["snomed_code"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
icdo_resolver,
chunkerMapper
])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
light_pipeline= LightPipeline(model)
result = light_pipeline.fullAnnotate("Hepatocellular Carcinoma")
val documentAssembler = new 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 icdo_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_icdo_augmented", "en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("icdo_code")
.setDistanceFunction("EUCLIDEAN")
val chunkerMapper = ChunkMapperModel
.pretrained("icdo_snomed_mapper", "en", "clinical/models")
.setInputCols(Array("icdo_code"))
.setOutputCol("snomed_mappings")
.setRels(Array("snomed_code"))
val pipeline = new Pipeline(stages = Array(
documentAssembler,
sbert_embedder,
icdo_resolver,
chunkerMapper
))
val data = Seq("Hepatocellular Carcinoma").toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.icdo_to_snomed").predict("""Hepatocellular Carcinoma""")
Results
| | ner_chunk | icdo_code | snomed_mappings |
|---:|:-------------------------|:------------|------------------:|
| 0 | Hepatocellular Carcinoma | 8170/3 | 25370001 |
Model Information
| Model Name: | icdo_snomed_mapper |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [icdo_code] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 127.9 KB |
References
This pretrained model maps ICDO codes to corresponding SNOMED codes under the Unified Medical Language System (UMLS).