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2023-05-21-ner_ade_emb_clinical_medium_en (#244)
* Add model 2023-05-21-ner_ade_emb_clinical_medium_en * Add model 2023-05-21-ner_ade_emb_clinical_large_en * Add model 2023-05-24-ner_cellular_emb_clinical_medium_en * Add model 2023-05-24-ner_cellular_emb_clinical_large_en --------- Co-authored-by: Damla-Gurbaz <dml.grbz.01@gmail.com>
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docs/_posts/Damla-Gurbaz/2023-05-21-ner_ade_emb_clinical_large_en.md
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--- | ||
layout: model | ||
title: Detect Adverse Drug Events (clinical_large) | ||
author: John Snow Labs | ||
name: ner_ade_emb_clinical_large | ||
date: 2023-05-21 | ||
tags: [ner, ade, drug, licensed, clinical, en] | ||
task: Named Entity Recognition | ||
language: en | ||
edition: Healthcare NLP 4.4.2 | ||
spark_version: 3.0 | ||
supported: true | ||
annotator: MedicalNerModel | ||
article_header: | ||
type: cover | ||
use_language_switcher: "Python-Scala-Java" | ||
--- | ||
|
||
## Description | ||
|
||
Detect adverse reactions to drugs in reviews, tweets, and medical text using a pre-trained NER model. | ||
|
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## Predicted Entities | ||
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`DRUG`, `ADE` | ||
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{:.btn-box} | ||
[Live Demo](https://demo.johnsnowlabs.com/healthcare/ADE/){:.button.button-orange} | ||
[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb){:.button.button-orange.button-orange-trans.co.button-icon} | ||
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_ade_emb_clinical_large_en_4.4.2_3.0_1684710290191.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_ade_emb_clinical_large_en_4.4.2_3.0_1684710290191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} | ||
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## How to use | ||
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<div class="tabs-box" markdown="1"> | ||
{% include programmingLanguageSelectScalaPythonNLU.html %} | ||
```python | ||
document_assembler = DocumentAssembler()\ | ||
.setInputCol("text")\ | ||
.setOutputCol("document") | ||
|
||
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\ | ||
.setInputCols(["document"])\ | ||
.setOutputCol("sentence") | ||
|
||
tokenizer = Tokenizer()\ | ||
.setInputCols(["sentence"])\ | ||
.setOutputCol("token") | ||
|
||
clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical_large", "en", "clinical/models")\ | ||
.setInputCols(["sentence", "token"])\ | ||
.setOutputCol("embeddings") | ||
|
||
ner_model = MedicalNerModel.pretrained("ner_ade_emb_clinical_large", "en", "clinical/models")\ | ||
.setInputCols(["sentence", "token","embeddings"])\ | ||
.setOutputCol("ner") | ||
|
||
ner_converter = NerConverterInternal()\ | ||
.setInputCols(['sentence', 'token', 'ner'])\ | ||
.setOutputCol('ner_chunk') | ||
|
||
pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
sentence_detector, | ||
tokenizer, | ||
clinical_embeddings, | ||
ner_model, | ||
ner_converter | ||
]) | ||
|
||
sample_df = spark.createDataFrame([["Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."]]).toDF("text") | ||
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result = pipeline.fit(sample_df).transform(sample_df) | ||
``` | ||
```scala | ||
val document_assembler = new DocumentAssembler() | ||
.setInputCol("text") | ||
.setOutputCol("document") | ||
|
||
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en") | ||
.setInputCols("document") | ||
.setOutputCol("sentence") | ||
|
||
val tokenizer = new Tokenizer() | ||
.setInputCols("sentence") | ||
.setOutputCol("token") | ||
|
||
val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical_large", "en", "clinical/models") | ||
.setInputCols(Array("sentence", "token")) | ||
.setOutputCol("embeddings") | ||
|
||
val ner_model = MedicalNerModel.pretrained("ner_ade_emb_clinical_large", "en", "clinical/models") | ||
.setInputCols(Array("sentence", "token", "embeddings")) | ||
.setOutputCol("ner") | ||
|
||
val ner_converter = new NerConverterInternal() | ||
.setInputCols(Array("sentence", "token", "ner")) | ||
.setOutputCol("ner_chunk") | ||
|
||
val pipeline = new Pipeline().setStages(Array( | ||
document_assembler, | ||
sentence_detector, | ||
tokenizer, | ||
clinical_embeddings, | ||
ner_model, | ||
ner_converter)) | ||
|
||
val sample_data = Seq("Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.").toDS.toDF("text") | ||
|
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val result = pipeline.fit(sample_data).transform(sample_data) | ||
``` | ||
</div> | ||
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## Results | ||
|
||
```bash | ||
+--------------+-----+---+---------+ | ||
|chunk |begin|end|ner_label| | ||
+--------------+-----+---+---------+ | ||
|Lipitor |12 |18 |DRUG | | ||
|severe fatigue|52 |65 |ADE | | ||
|voltaren |97 |104|DRUG | | ||
|cramps |152 |157|ADE | | ||
+--------------+-----+---+---------+ | ||
``` | ||
|
||
{:.model-param} | ||
## Model Information | ||
|
||
{:.table-model} | ||
|---|---| | ||
|Model Name:|ner_ade_emb_clinical_large| | ||
|Compatibility:|Healthcare NLP 4.4.2+| | ||
|License:|Licensed| | ||
|Edition:|Official| | ||
|Input Labels:|[sentence, token, embeddings]| | ||
|Output Labels:|[ner]| | ||
|Language:|en| | ||
|Size:|2.7 MB| | ||
|
||
## Benchmarking | ||
|
||
```bash | ||
label precision recall f1-score support | ||
DRUG 0.92 0.91 0.92 16032 | ||
ADE 0.82 0.80 0.81 6142 | ||
micro-avg 0.89 0.88 0.89 22174 | ||
macro-avg 0.87 0.86 0.86 22174 | ||
weighted-avg 0.89 0.88 0.89 22174 | ||
``` |
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docs/_posts/Damla-Gurbaz/2023-05-21-ner_ade_emb_clinical_medium_en.md
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@@ -0,0 +1,152 @@ | ||
--- | ||
layout: model | ||
title: Detect Adverse Drug Events (clinical_medium) | ||
author: John Snow Labs | ||
name: ner_ade_emb_clinical_medium | ||
date: 2023-05-21 | ||
tags: [en, clinical, ade, drug, licensed, ner] | ||
task: Named Entity Recognition | ||
language: en | ||
edition: Healthcare NLP 4.4.2 | ||
spark_version: 3.0 | ||
supported: true | ||
annotator: MedicalNerModel | ||
article_header: | ||
type: cover | ||
use_language_switcher: "Python-Scala-Java" | ||
--- | ||
|
||
## Description | ||
|
||
Detect adverse reactions to drugs in reviews, tweets, and medical text using a pre-trained NER model. | ||
|
||
## Predicted Entities | ||
|
||
`DRUG`, `ADE` | ||
|
||
{:.btn-box} | ||
[Live Demo](https://demo.johnsnowlabs.com/healthcare/ADE/){:.button.button-orange} | ||
[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb){:.button.button-orange.button-orange-trans.co.button-icon} | ||
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_ade_emb_clinical_medium_en_4.4.2_3.0_1684646733993.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_ade_emb_clinical_medium_en_4.4.2_3.0_1684646733993.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") | ||
|
||
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\ | ||
.setInputCols(["document"])\ | ||
.setOutputCol("sentence") | ||
|
||
tokenizer = Tokenizer()\ | ||
.setInputCols(["sentence"])\ | ||
.setOutputCol("token") | ||
|
||
clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical_medium", "en", "clinical/models")\ | ||
.setInputCols(["sentence", "token"])\ | ||
.setOutputCol("embeddings") | ||
|
||
ner_model = MedicalNerModel.pretrained("ner_ade_emb_clinical_medium", "en", "clinical/models")\ | ||
.setInputCols(["sentence", "token", "embeddings"])\ | ||
.setOutputCol("ner") | ||
|
||
ner_converter = NerConverterInternal()\ | ||
.setInputCols(['sentence', 'token', 'ner'])\ | ||
.setOutputCol('ner_chunk') | ||
|
||
pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
sentence_detector, | ||
tokenizer, | ||
clinical_embeddings, | ||
ner_model, | ||
ner_converter | ||
]) | ||
|
||
sample_df = spark.createDataFrame([["Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."]]).toDF("text") | ||
|
||
result = pipeline.fit(sample_df).transform(sample_df) | ||
``` | ||
```scala | ||
val document_assembler = new DocumentAssembler() | ||
.setInputCol("text") | ||
.setOutputCol("document") | ||
|
||
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en") | ||
.setInputCols("document") | ||
.setOutputCol("sentence") | ||
|
||
val tokenizer = new Tokenizer() | ||
.setInputCols("sentence") | ||
.setOutputCol("token") | ||
|
||
val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical_medium", "en", "clinical/models") | ||
.setInputCols(Array("sentence", "token")) | ||
.setOutputCol("embeddings") | ||
|
||
val ner_model = MedicalNerModel.pretrained("ner_ade_emb_clinical_medium", "en", "clinical/models") | ||
.setInputCols(Array("sentence", "token", "embeddings")) | ||
.setOutputCol("ner") | ||
|
||
val ner_converter = new NerConverterInternal() | ||
.setInputCols(Array("sentence", "token", "ner")) | ||
.setOutputCol("ner_chunk") | ||
|
||
val pipeline = new Pipeline().setStages(Array( | ||
document_assembler, | ||
sentence_detector, | ||
tokenizer, | ||
clinical_embeddings, | ||
ner_model, | ||
ner_converter)) | ||
|
||
val sample_data = Seq("Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.").toDS.toDF("text") | ||
|
||
val result = pipeline.fit(sample_data).transform(sample_data) | ||
``` | ||
</div> | ||
|
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## Results | ||
|
||
```bash | ||
+--------------+-----+---+---------+ | ||
|chunk |begin|end|ner_label| | ||
+--------------+-----+---+---------+ | ||
|Lipitor |12 |18 |DRUG | | ||
|severe fatigue|52 |65 |ADE | | ||
|voltaren |97 |104|DRUG | | ||
|cramps |152 |157|ADE | | ||
+--------------+-----+---+---------+ | ||
``` | ||
|
||
{:.model-param} | ||
## Model Information | ||
|
||
{:.table-model} | ||
|---|---| | ||
|Model Name:|ner_ade_emb_clinical_medium| | ||
|Compatibility:|Healthcare NLP 4.4.2+| | ||
|License:|Licensed| | ||
|Edition:|Official| | ||
|Input Labels:|[sentence, token, embeddings]| | ||
|Output Labels:|[ner]| | ||
|Language:|en| | ||
|Size:|2.7 MB| | ||
|
||
## Benchmarking | ||
|
||
```bash | ||
label precision recall f1-score support | ||
DRUG 0.92 0.91 0.91 15895 | ||
ADE 0.83 0.77 0.80 6077 | ||
micro-avg 0.89 0.87 0.88 21972 | ||
macro-avg 0.87 0.84 0.86 21972 | ||
weighted-avg 0.89 0.87 0.88 21972 | ||
``` |
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