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2023-05-26-icd10cm_billable_hcc_mapper_en #266

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105 changes: 105 additions & 0 deletions docs/_posts/Ahmetemintek/2023-05-26-icd10cm_billable_hcc_mapper_en.md
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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|