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Co-authored-by: Ahmetemintek <ahmetemin.tek.66@gmail.com>

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Co-authored-by: hsaglamlar <hsaglamlar@gmail.com>
Co-authored-by: Cabir ÇELİK <ogr.cabir.celik@ahievran.edu.tr>
Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com>

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Co-authored-by: jsl-models <74001263+jsl-models@users.noreply.github.com>
Co-authored-by: Ahmetemintek <ahmetemin.tek.66@gmail.com>
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92 changes: 92 additions & 0 deletions docs/_posts/Ahmetemintek/2023-03-29-icd10cm_resolver_pipeline_en.md
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---
layout: model
title: Pipeline to Resolve ICD-10-CM Codes
author: John Snow Labs
name: icd10cm_resolver_pipeline
date: 2023-03-29
tags: [en, licensed, clinical, resolver, chunk_mapping, pipeline, icd10cm]
task: Pipeline Healthcare
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.2
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This pretrained pipeline maps entities with their corresponding ICD-10-CM codes. You’ll just feed your text and it will return the corresponding ICD-10-CM codes.

{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icd10cm_resolver_pipeline_en_4.3.2_3.2_1680105059178.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icd10cm_resolver_pipeline_en_4.3.2_3.2_1680105059178.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
from sparknlp.pretrained import PretrainedPipeline

resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")

text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""

result = resolver_pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")

val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
```
</div>

## Results

```bash
+-----------------------------+---------+------------+
|chunk |ner_chunk|icd10cm_code|
+-----------------------------+---------+------------+
|gestational diabetes mellitus|PROBLEM |O24.919 |
|anisakiasis |PROBLEM |B81.0 |
|fetal and neonatal hemorrhage|PROBLEM |P545 |
+-----------------------------+---------+------------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|icd10cm_resolver_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|3.5 GB|

## Included Models

- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
87 changes: 87 additions & 0 deletions docs/_posts/Ahmetemintek/2023-03-29-umls_drug_resolver_pipeline_en.md
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---
layout: model
title: Clinical Drugs to UMLS Code Mapping
author: John Snow Labs
name: umls_drug_resolver_pipeline
date: 2023-03-29
tags: [en, licensed, umls, pipeline]
task: Pipeline Healthcare
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.2
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This pretrained pipeline maps entities (Clinical Drugs) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.

{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_drug_resolver_pipeline_en_4.3.2_3.2_1680127003093.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_drug_resolver_pipeline_en_4.3.2_3.2_1680127003093.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
from sparknlp.pretrained import PretrainedPipeline

pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
```
</div>

## Results

```bash
+------------+---------+---------+
|chunk |ner_label|umls_code|
+------------+---------+---------+
|Adapin 10 MG|DRUG |C2930083 |
|coumadn 5 mg|DRUG |C2723075 |
+------------+---------+---------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|umls_drug_resolver_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|4.6 GB|

## Included Models

- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
91 changes: 91 additions & 0 deletions docs/_posts/Ahmetemintek/2023-03-30-cvx_resolver_pipeline_en.md
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---
layout: model
title: Pipeline to Resolve CVX Codes
author: John Snow Labs
name: cvx_resolver_pipeline
date: 2023-03-30
tags: [en, licensed, clinical, resolver, chunk_mapping, cvx, pipeline]
task: Pipeline Healthcare
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.2
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This pretrained pipeline maps entities with their corresponding CVX codes. You’ll just feed your text and it will return the corresponding CVX codes.

{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/cvx_resolver_pipeline_en_4.3.2_3.2_1680178011294.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/cvx_resolver_pipeline_en_4.3.2_3.2_1680178011294.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
from sparknlp.pretrained import PretrainedPipeline

resolver_pipeline = PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")

text= "The patient has a history of influenza vaccine, tetanus and DTaP"

result = resolver_pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val resolver_pipeline = new PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")

val result = resolver_pipeline.fullAnnotate("The patient has a history of influenza vaccine, tetanus and DTaP")
```
</div>

## Results

```bash
+-----------------+---------+--------+
|chunk |ner_chunk|cvx_code|
+-----------------+---------+--------+
|influenza vaccine|Vaccine |160 |
|tetanus |Vaccine |35 |
|DTaP |Vaccine |20 |
+-----------------+---------+--------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|cvx_resolver_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|2.1 GB|

## Included Models

- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
94 changes: 94 additions & 0 deletions docs/_posts/Ahmetemintek/2023-03-30-umls_clinical_findings_resolver_pipeline_en.md
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---
layout: model
title: Clinical Findings to UMLS Code Pipeline
author: John Snow Labs
name: umls_clinical_findings_resolver_pipeline
date: 2023-03-30
tags: [en, licensed, umls, resolver, pipeline, clinical]
task: Pipeline Healthcare
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.2
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This pretrained pipeline maps entities (Clinical Findings) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.

{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_4.3.2_3.2_1680168336993.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_4.3.2_3.2_1680168336993.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
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")

text = '''['HTG-induced pancreatitis associated with an acute hepatitis, and obesity']'''

result = pipeline.annotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")

val text = "HTG-induced pancreatitis associated with an acute hepatitis, and obesity"

val result = pipeline.annotate(text)
```
</div>

## Results

```bash
+------------------------+---------+---------+
|chunk |ner_label|umls_code|
+------------------------+---------+---------+
|HTG-induced pancreatitis|PROBLEM |C1963198 |
|an acute hepatitis |PROBLEM |C4750596 |
|obesity |PROBLEM |C1963185 |
+------------------------+---------+---------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|umls_clinical_findings_resolver_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|4.3 GB|

## Included Models

- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
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