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2023-02-10-ner_sdoh_social_environment_wip_en #13496

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166 changes: 166 additions & 0 deletions docs/_posts/Meryem1425/2023-02-10-ner_sdoh_demographics_wip_en.md
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---
layout: model
title: Extract Demographic Entities from Social Determinants of Health Texts
author: John Snow Labs
name: ner_sdoh_demographics_wip
date: 2023-02-10
tags: [licensed, clinical, social_determinants, en, ner, demographics, sdoh, public_health]
task: Named Entity Recognition
language: en
edition: Healthcare NLP 4.2.8
spark_version: 3.0
supported: true
annotator: MedicalNerModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This model extracts demographic information related to Social Determinants of Health from various kinds of biomedical documents.

## Predicted Entities

`Family_Member`, `Age`, `Gender`, `Geographic_Entity`, `Race_Ethnicity`, `Language`, `Spiritual_Beliefs`

{:.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/ner_sdoh_demographics_wip_en_4.2.8_3.0_1675998706136.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_sdoh_demographics_wip_en_4.2.8_3.0_1675998706136.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", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")

ner_model = MedicalNerModel.pretrained("ner_sdoh_demographics_wip", "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_texts = ["SOCIAL HISTORY: He is a former tailor from Korea.",
"He lives alone,single and no children.",
"Pt is a 61 years old married, Caucasian, Catholic woman. Pt speaks English reasonably well."]


data = spark.createDataFrame(sample_texts, StringType()).toDF("text")

result = pipeline.fit(data).transform(data)
```
```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", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")

val ner_model = MedicalNerModel.pretrained("ner_sdoh_demographics_wip", "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 data = Seq("Pt is a 61 years old married, Caucasian, Catholic woman. Pt speaks English reasonably well.").toDS.toDF("text")

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

## Results

```bash
+-----------------+-----+---+------------+
|ner_label |begin|end|chunk |
+-----------------+-----+---+------------+
|Gender |16 |17 |He |
|Geographic_Entity|43 |47 |Korea |
|Gender |0 |1 |He |
|Family_Member |29 |36 |children |
|Age |8 |19 |61 years old|
|Race_Ethnicity |30 |38 |Caucasian |
|Spiritual_Beliefs|41 |48 |Catholic |
|Gender |50 |54 |woman |
|Language |67 |73 |English |
+-----------------+-----+---+------------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|ner_sdoh_demographics_wip|
|Compatibility:|Healthcare NLP 4.2.8+|
|License:|Licensed|
|Edition:|Official|
|Input Labels:|[sentence, token, embeddings]|
|Output Labels:|[ner]|
|Language:|en|
|Size:|858.4 KB|

## Benchmarking

```bash
label tp fp fn total precision recall f1
Age 1346.0 73.0 74.0 1420.0 0.948555 0.947887 0.948221
Spiritual_Beliefs 100.0 13.0 16.0 116.0 0.884956 0.862069 0.873362
Family_Member 4468.0 134.0 43.0 4511.0 0.970882 0.990468 0.980577
Race_Ethnicity 56.0 0.0 13.0 69.0 1.000000 0.811594 0.896000
Gender 9825.0 67.0 247.0 10072.0 0.993227 0.975477 0.984272
Geographic_Entity 225.0 9.0 29.0 254.0 0.961538 0.885827 0.922131
Language 51.0 9.0 5.0 56.0 0.850000 0.910714 0.879310
```
157 changes: 157 additions & 0 deletions docs/_posts/Meryem1425/2023-02-10-ner_sdoh_income_social_status_wip_en.md
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---
layout: model
title: Extract Income and Social Status Entities from Social Determinants of Health Texts
author: John Snow Labs
name: ner_sdoh_income_social_status_wip
date: 2023-02-10
tags: [licensed, clinical, social_determinants, en, ner, income, social_status, sdoh, public_health]
task: Named Entity Recognition
language: en
edition: Healthcare NLP 4.2.8
spark_version: 3.0
supported: true
annotator: MedicalNerModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This model extracts income and social status information related to Social Determinants of Health from various kinds of biomedical documents.

## Predicted Entities

`Education`, `Marital_Status`, `Financial_Status`, `Population_Group`, `Employment`

{:.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/ner_sdoh_income_social_status_wip_en_4.2.8_3.0_1675999206708.zip){:.button.button-orange}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_sdoh_income_social_status_wip_en_4.2.8_3.0_1675999206708.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", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")

ner_model = MedicalNerModel.pretrained("ner_sdoh_income_social_status_wip", "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_texts = ["Pt is described as divorced and pleasant when approached but keeps to himself. Pt is working as a plumber, but he gets financial diffuculties. He has a son student at college. His family is imigrant for 2 years."]

data = spark.createDataFrame(sample_texts, StringType()).toDF("text")

result = pipeline.fit(data).transform(data)
```
```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", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")

val ner_model = MedicalNerModel.pretrained("ner_sdoh_income_social_status_wip", "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 data = Seq("Pt is described as divorced and pleasant when approached but keeps to himself. Pt is working as a plumber, but he gets financial diffuculties. He has a son student at college. His family is imigrant for 2 years.").toDS.toDF("text")

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

## Results

```bash
+-----------+----------------+-----+---+----------------------+
|sentence_id|ner_label |begin|end|chunk |
+-----------+----------------+-----+---+----------------------+
|0 |Marital_Status |19 |26 |divorced |
|1 |Employment |98 |104|plumber |
|1 |Financial_Status|119 |140|financial diffuculties|
|2 |Education |156 |162|student |
|2 |Education |167 |173|college |
|3 |Population_Group|190 |197|imigrant |
+-----------+----------------+-----+---+----------------------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|ner_sdoh_income_social_status_wip|
|Compatibility:|Healthcare NLP 4.2.8+|
|License:|Licensed|
|Edition:|Official|
|Input Labels:|[sentence, token, embeddings]|
|Output Labels:|[ner]|
|Language:|en|
|Size:|856.8 KB|

## Benchmarking

```bash
label tp fp fn total precision recall f1
Education 95.0 20.0 18.0 113.0 0.826087 0.840708 0.833333
Population_Group 41.0 0.0 5.0 46.0 1.000000 0.891304 0.942529
Financial_Status 286.0 52.0 82.0 368.0 0.846154 0.777174 0.810198
Employment 3968.0 142.0 215.0 4183.0 0.965450 0.948601 0.956952
Marital_Status 167.0 1.0 7.0 174.0 0.994048 0.959770 0.976608
```
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