2024-02-26-umls_snomed_mapper_en

docs/_posts/ahmet-mesut/2024-02-26-snomed_umls_mapper_en.md

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+---
+layout: model
+title: Mapping SNOMED Codes with Their Corresponding UMLS Codes
+author: John Snow Labs
+name: snomed_umls_mapper
+date: 2024-02-26
+tags: [licensed, en, umls, snomed, mapping]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 5.2.1
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps SNOMED codes to corresponding UMLS codes.
+
+## Predicted Entities
+
+`umls_code`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_umls_mapper_en_5.2.1_3.0_1708984203970.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_umls_mapper_en_5.2.1_3.0_1708984203970.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+documentAssembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("ner_chunk")
+
+sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
+    .setInputCols(["ner_chunk"])\
+    .setOutputCol("sbert_embeddings")\
+    .setCaseSensitive(False)
+
+snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_drug", "en", "clinical/models")\
+    .setInputCols(["sbert_embeddings"]) \
+    .setOutputCol("snomed_code")\
+    .setDistanceFunction("EUCLIDEAN")
+
+resolver2chunk = Resolution2Chunk()\
+    .setInputCols(["snomed_code"])\
+    .setOutputCol("snomed2chunk")
+
+chunkerMapper = ChunkMapperModel.pretrained("snomed_umls_mapper", "en", "clinical/models")\
+    .setInputCols(["umls2chunk"])\
+    .setOutputCol("mappings")
+
+pipeline = Pipeline(stages = [
+    documentAssembler,
+    sbert_embedder,
+    snomed_resolver,
+    resolver2chunk,
+    chunkerMapper])
+
+
+data = spark.createDataFrame([["acebutolol"],["aspirin"]]).toDF("text")
+
+mapper_model = pipeline.fit(data)
+result= mapper_model.transform(data)  
+```
+```scala
+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")
+    .setCaseSensitive(false)
+
+val snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_drug","en","clinical/models")
+    .setInputCols(Array("sbert_embeddings"))
+    .setOutputCol("snomed_code")
+    .setDistanceFunction("EUCLIDEAN")
+
+val resolver2chunk = new Resolution2Chunk()
+    .setInputCols(Array("snomed_code"))
+    .setOutputCol("snomed2chunk")
+
+val chunkerMapper = ChunkMapperModel.pretrained("snomed_umls_mapper","en","clinical/models")
+    .setInputCols(Array("umls2chunk"))
+    .setOutputCol("mappings")
+
+val Pipeline(stages = Array(
+     documentAssembler,
+     sbert_embedder,
+     snomed_resolver,
+     resolver2chunk,
+     chunkerMapper))
+
+val data = Seq("acebutolol", "aspirin").toDF("text")
+
+val mapper_model = pipeline.fit(data)
+result= mapper_model.transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------+-----------+---------+
+|chunk     |snomed_code|umls_code|
++----------+-----------+---------+
+|acebutolol|372815001  |C0000946 |
+|aspirin   |387458008  |C0004057 |
++----------+-----------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|snomed_umls_mapper|
+|Compatibility:|Healthcare NLP 5.2.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|7.5 MB|

docs/_posts/ahmet-mesut/2024-02-26-umls_snomed_mapper_en.md

@@ -0,0 +1,136 @@
+---
+layout: model
+title: Mapping UMLS Codes with Their Corresponding SNOMED Codes
+author: John Snow Labs
+name: umls_snomed_mapper
+date: 2024-02-26
+tags: [en, licensed, umls, snomed, mapping]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 5.2.1
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps UMLS codes to corresponding SNOMED codes.
+
+## Predicted Entities
+
+`snomed`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_snomed_mapper_en_5.2.1_3.0_1708981559297.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_snomed_mapper_en_5.2.1_3.0_1708981559297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+documentAssembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("ner_chunk")
+
+sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
+    .setInputCols(["ner_chunk"])\
+    .setOutputCol("sbert_embeddings")\
+    .setCaseSensitive(False)
+
+umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_clinical_drugs", "en", "clinical/models")\
+    .setInputCols(["sbert_embeddings"]) \
+    .setOutputCol("umls_code")\
+    .setDistanceFunction("EUCLIDEAN")
+
+resolver2chunk = Resolution2Chunk()\
+    .setInputCols(["umls_code"])\
+    .setOutputCol("umls2chunk")
+
+chunkerMapper = ChunkMapperModel.pretrained("umls_snomed_mapper", "en", "clinical/models")\
+    .setInputCols(["umls2chunk"])\
+    .setOutputCol("mappings")
+
+pipeline = Pipeline(stages = [
+    documentAssembler,
+    sbert_embedder,
+    umls_resolver,
+    resolver2chunk,
+    chunkerMapper])
+
+data = spark.createDataFrame([["acebutolol"],["aspirin"]]).toDF("text")
+
+mapper_model = pipeline.fit(data)
+result= mapper_model.transform(data)  
+```
+```scala
+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")
+    .setCaseSensitive(false)
+
+val umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_clinical_drugs","en","clinical/models")
+    .setInputCols(Array("sbert_embeddings"))
+    .setOutputCol("umls_code")
+    .setDistanceFunction("EUCLIDEAN")
+
+val resolver2chunk = new Resolution2Chunk()
+    .setInputCols(Array("umls_code"))
+    .setOutputCol("umls2chunk")
+
+val chunkerMapper = ChunkMapperModel.pretrained("umls_snomed_mapper","en","clinical/models")
+    .setInputCols(Array("umls2chunk"))
+    .setOutputCol("mappings")
+
+val Pipeline(stages = Array(
+  documentAssembler,
+  sbert_embedder, 
+  umls_resolver,
+  resolver2chunk,
+  chunkerMapper))
+
+val data = Seq("acebutolol", "aspirin").toDF("text")
+
+val mapper_model = pipeline.fit(data)
+
+result= mapper_model.transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------+---------+-----------+
+|chunk     |umls_code|snomed_code|
++----------+---------+-----------+
+|acebutolol|C0000946 |68088000   |
+|aspirin   |C0004057 |319770009  |
++----------+---------+-----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_snomed_mapper|
+|Compatibility:|Healthcare NLP 5.2.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|6.9 MB|