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2023-04-10-medication_resolver_pipeline_en #83

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Add model 2023-04-10-medication_resolver_pipeline_en
SKocer Apr 10, 2023
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Add model 2023-04-11-medication_resolver_transform_pipeline_en
SKocer Apr 11, 2023
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105 changes: 105 additions & 0 deletions docs/_posts/SKocer/2023-04-10-medication_resolver_pipeline_en.md
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---
layout: model
title: Pipeline to Resolve Medication Codes
author: John Snow Labs
name: medication_resolver_pipeline
date: 2023-04-10
tags: [resolver, snomed, umls, rxnorm, ndc, ade, en, licensed, pipeline]
task: Entity Resolution
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.0
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.

Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.

This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You can use `medication_resolver_transform_pipeline` for Spark transform.

{:.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/medication_resolver_pipeline_en_4.3.2_3.0_1681151954032.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_4.3.2_3.0_1681151954032.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

med_resolver_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")

text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""

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

val med_resolver_pipeline = new PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")

val result = med_resolver_pipeline.fullAnnotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""")
```
</div>

## Results

```bash
| | chunks | entities | ADE | RxNorm | Action | Treatment | UMLS | SNOMED_CT | NDC_Product | NDC_Package |
|---:|:-----------------------------|:-----------|:----------------------------|---------:|:---------------------------|:-------------------------------------------|:---------|:------------|:--------------|:--------------|
| 0 | Amlodopine Vallarta 10-320mg | DRUG | Gynaecomastia | 722131 | NONE | NONE | C1949334 | 425838008 | 00093-7693 | 00093-7693-56 |
| 1 | Eviplera | DRUG | Anxiety | 217010 | Inhibitory Bone Resorption | Osteoporosis | C0720318 | NONE | NONE | NONE |
| 2 | Lescol 40 MG | DRUG | NONE | 103919 | Hypocholesterolemic | Heterozygous Familial Hypercholesterolemia | C0353573 | NONE | 00078-0234 | 00078-0234-05 |
| 3 | Everolimus 1.5 mg tablet | DRUG | Acute myocardial infarction | 2056895 | NONE | NONE | C4723581 | NONE | 00054-0604 | 00054-0604-21 |
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|medication_resolver_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|3.2 GB|

## Included Models

- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- TextMatcherModel
- ChunkMergeModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
- ResolverMerger
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- Finisher
111 changes: 111 additions & 0 deletions docs/_posts/SKocer/2023-04-11-medication_resolver_transform_pipeline_en.md
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---
layout: model
title: Pipeline to Resolve Medication Codes(Transform)
author: John Snow Labs
name: medication_resolver_transform_pipeline
date: 2023-04-11
tags: [resolver, rxnorm, ndc, snomed, umls, ade, pipeline, en, licensed]
task: Entity Resolution
language: en
edition: Healthcare NLP 4.3.2
spark_version: 3.0
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.

Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.

This pipeline can be used with Spark transform. You can use `medication_resolver_pipeline` as Lightpipeline (with `annotate/fullAnnotate`).

{:.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/medication_resolver_transform_pipeline_en_4.3.2_3.0_1681190723377.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_4.3.2_3.0_1681190723377.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

medication_resolver_pipeline = PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")

text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""

data = spark.createDataFrame([[text]]).toDF("text")

result = medication_resolver_pipeline.transform(data)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val medication_resolver_pipeline = new PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")

val data = Seq("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""").toDS.toDF("text")

val result = medication_resolver_pipeline.fit(data).transform(data)
```
</div>

## Results

```bash
| chunk | ner_label | ADE | RxNorm | Action | Treatment | UMLS | SNOMED_CT | NDC_Product | NDC_Package |
|:-----------------------------|:------------|:----------------------------|---------:|:---------------------------|:-------------------------------------------|:---------|:------------|:--------------|:--------------|
| Amlodopine Vallarta 10-320mg | DRUG | Gynaecomastia | 722131 | NONE | NONE | C1949334 | 425838008 | 00093-7693 | 00093-7693-56 |
| Eviplera | DRUG | Anxiety | 217010 | Inhibitory Bone Resorption | Osteoporosis | C0720318 | NONE | NONE | NONE |
| Lescol 40 MG | DRUG | NONE | 103919 | Hypocholesterolemic | Heterozygous Familial Hypercholesterolemia | C0353573 | NONE | 00078-0234 | 00078-0234-05 |
| Everolimus 1.5 mg tablet | DRUG | Acute myocardial infarction | 2056895 | NONE | NONE | C4723581 | NONE | 00054-0604 | 00054-0604-21 |
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|medication_resolver_transform_pipeline|
|Type:|pipeline|
|Compatibility:|Healthcare NLP 4.3.2+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|3.2 GB|

## Included Models

- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- TextMatcherModel
- ChunkMergeModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger
- Doc2Chunk
- ResolverMerger
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- Doc2Chunk
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- Finisher