2023-04-13-ndc_hcpcs_mapper_en

docs/_posts/Ahmetemintek/2023-04-13-hcpcs_ndc_mapper_en.md

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+---
+layout: model
+title: Mapping HCPCS Codes  with Corresponding National Drug Codes (NDC) and Drug Brand Names
+author: John Snow Labs
+name: hcpcs_ndc_mapper
+date: 2023-04-13
+tags: [en, licensed, chunk_mappig, hcpcs, ndc, brand_name]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.4.0
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps HCPCS codes with their corresponding National Drug Codes (NDC) and their drug brand names.
+
+## Predicted Entities
+
+`ndc_code`, `brand_name`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/hcpcs_ndc_mapper_en_4.4.0_3.0_1681405950608.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/hcpcs_ndc_mapper_en_4.4.0_3.0_1681405950608.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
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("hcpcs_chunk")
+
+chunkerMapper = DocMapperModel.pretrained("hcpcs_ndc_mapper", "en", "clinical/models")\
+      .setInputCols(["hcpcs_chunk"])\
+      .setOutputCol("mappings")\
+      .setRels(["ndc_code", "brand_name"])
+
+pipeline = Pipeline().setStages([document_assembler,
+                                 chunkerMapper])  
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+lp = LightPipeline(model)
+
+res = lp.fullAnnotate(["Q5106", "J9211", "J7508"])
+```
+```scala
+val document_assembler = new DocumentAssembler()
+       .setInputCol("text")\
+       .setOutputCol("hcpcs_chunk")
+
+val chunkerMapper = DocMapperModel
+       .pretrained("hcpcs_ndc_mapper", "en", "clinical/models")
+       .setInputCols(Array("hcpcs_chunk"))
+       .setOutputCol("mappings")
+       .setRels(Array("ndc_code", "brand_name")) 
+
+val mapper_pipeline = new Pipeline().setStages(Array(
+                                                   document_assembler,
+                                                   chunkerMapper))
+
+
+val data = Seq(Array(["Q5106", "J9211", "J7508"])).toDS.toDF("text")
+val result = pipeline.fit(data).transform(data) 
+```
+</div>
+
+## Results
+
+```bash
++-----------+-------------------------------------+----------+
+|hcpcs_chunk|mappings                             |relation  |
++-----------+-------------------------------------+----------+
+|Q5106      |59353-0003-10                        |ndc_code  |
+|Q5106      |RETACRIT (PF) 3000 U/1 ML            |brand_name|
+|J9211      |59762-2596-01                        |ndc_code  |
+|J9211      |IDARUBICIN HYDROCHLORIDE (PF) 1 MG/ML|brand_name|
+|J7508      |00469-0687-73                        |ndc_code  |
+|J7508      |ASTAGRAF XL 5 MG                     |brand_name|
++-----------+-------------------------------------+----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|hcpcs_ndc_mapper|
+|Compatibility:|Healthcare NLP 4.4.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|20.7 KB|

docs/_posts/Ahmetemintek/2023-04-13-ndc_hcpcs_mapper_en.md

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+---
+layout: model
+title: Mapping National Drug Codes (NDC) with Corresponding HCPCS Codes and Descriptions
+author: John Snow Labs
+name: ndc_hcpcs_mapper
+date: 2023-04-13
+tags: [en, licensed, clinical, chunk_mapping, ndc, hcpcs, brand_name]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.4.0
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps National Drug Codes (NDC) with their corresponding HCPCS codes and their descriptions.
+
+## Predicted Entities
+
+`hcpcs_code`, `hcpcs_description`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ndc_hcpcs_mapper_en_4.4.0_3.0_1681405091593.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ndc_hcpcs_mapper_en_4.4.0_3.0_1681405091593.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
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("ndc_chunk")
+
+chunkerMapper = DocMapperModel.pretrained("ndc_hcpcs_mapper", "en", "clinical/models")\
+      .setInputCols(["ndc_chunk"])\
+      .setOutputCol("hcpcs")\
+      .setRels(["hcpcs_code", "hcpcs_description"])
+
+pipeline = Pipeline().setStages([document_assembler,
+                                 chunkerMapper])  
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+lp = LightPipeline(model)
+
+res = lp.fullAnnotate(["16714-0892-01", "00990-6138-03", "43598-0650-11"])
+```
+```scala
+val document_assembler = new DocumentAssembler()
+       .setInputCol("text")\
+       .setOutputCol("ndc_chunk")
+
+val chunkerMapper = DocMapperModel
+       .pretrained("ndc_hcpcs_mapper", "en", "clinical/models")
+       .setInputCols(Array("ndc_chunk"))
+       .setOutputCol("mappings")
+       .setRels(Array("hcpcs_code", "hcpcs_description")) 
+
+val mapper_pipeline = new Pipeline().setStages(Array(
+                                                   document_assembler,
+                                                   chunkerMapper))
+
+
+val data = Seq(Array("16714-0892-01", "00990-6138-03", "43598-0650-11")).toDS.toDF("text")
+val result = pipeline.fit(data).transform(data) 
+```
+</div>
+
+## Results
+
+```bash
++-------------+----------------------------+-----------------+
+|ndc_chunk    |mappings                    |relation         |
++-------------+----------------------------+-----------------+
+|16714-0892-01|J0878                       |hcpcs_code       |
+|16714-0892-01|INJECTION, DAPTOMYCIN, 1 MG |hcpcs_description|
+|00990-6138-03|A4217                       |hcpcs_code       |
+|00990-6138-03|STERILE WATER/SALINE, 500 ML|hcpcs_description|
+|43598-0650-11|J9340                       |hcpcs_code       |
+|43598-0650-11|INJECTION, THIOTEPA, 15 MG  |hcpcs_description|
++-------------+----------------------------+-----------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ndc_hcpcs_mapper|
+|Compatibility:|Healthcare NLP 4.4.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|203.1 KB|