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+---
+layout: model
+title: Mapping ICD-10-CM Codes with Corresponding Billable and Hierarchical Condition Category (HCC) Scores
+author: John Snow Labs
+name: icd10cm_billable_hcc_mapper
+date: 2023-05-26
+tags: [icd10cm, billable, hcc_score, clinical, en, chunk_mapping, licensed]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: DocMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps ICD-10-CM codes with their corresponding billable and HCC scores. If there is no HCC score for the corresponding icd-10-cm code, result will be returned as 0.
+
+## Predicted Entities
+
+`billable`, `hcc_score`
+
+{:.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/icd10cm_billable_hcc_mapper_en_4.4.2_3.0_1685107034729.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icd10cm_billable_hcc_mapper_en_4.4.2_3.0_1685107034729.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("document")
+
+docMapper= DocMapperModel().pretrained("icd10cm_billable_hcc_mapper", "en", "clinical/models")\
+    .setInputCols(["document"])\
+    .setOutputCol("mappings")\
+    .setRels(["billable", "hcc_score"]) \
+    .setLowerCase(True) \
+    .setMultivaluesRelations(True)
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    docMapper])
+
+
+data = spark.createDataFrame([["D66"], ["S22.00"], ["Z3A.10"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val docMapper = DocMapperModel.pretrained("icd10cm_billable_hcc_mapper", "en", "clinical/models")
+    .setInputCols("document")
+    .setOutputCol("mappings")
+    .setRels(Array(["billable", "hcc_score"]))
+
+val pipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    docMapper))
+
+
+val data = Seq(["D66"], ["S22.00"], ["Z3A.10"]).toDS.toDF("text")
+
+val result= pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++--------------+----------+-----------+
+| icd10cm_code | billable | hcc_score |
++--------------+----------+-----------+
+| D66          | 1        | 46        |
+| S22.00       | 0        | 0         |
+| Z3A.10       | 1        | 0         |
++--------------+----------+-----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|icd10cm_billable_hcc_mapper|
+|Compatibility:|Healthcare NLP 4.4.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|1.1 MB|