2022-11-16-abbreviation_category_mapper_en

docs/_posts/Ahmetemintek/2022-11-16-abbreviation_category_mapper_en.md

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+---
+layout: model
+title: Mapping Abbreviations and Acronyms of Medical Regulatory Activities with Their Definitions and Categories
+author: John Snow Labs
+name: abbreviation_category_mapper
+date: 2022-11-16
+tags: [abbreviation, definition, category, licensed, en, clinical, chunk_mapper]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.2.1
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps abbreviations and acronyms of medical regulatory activities with their definitions and categories. Predicted categories: `general`, `problem`, `test`, `treatment`, `medical_condition`, `clinical_dept`, `drug`, `nursing`, `internal_organ_or_component`, `hospital_unit`, `drug_frequency`, `employment`, `procedure`
+
+## Predicted Entities
+
+`definition`, `category`
+
+{:.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/abbreviation_category_mapper_en_4.2.1_3.0_1668594867892.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_detector = SentenceDetector()\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence")
+
+tokenizer = Tokenizer()\
+    .setInputCols("sentence")\
+    .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
+    .setInputCols(["sentence", "token"])\
+    .setOutputCol("embeddings")
+
+abbr_ner = MedicalNerModel.pretrained("ner_abbreviation_clinical", "en", "clinical/models") \
+    .setInputCols(["sentence", "token", "embeddings"]) \
+    .setOutputCol("abbr_ner")
+
+abbr_converter = NerConverter() \
+    .setInputCols(["sentence", "token", "abbr_ner"]) \
+    .setOutputCol("abbr_ner_chunk")\
+
+chunkerMapper = ChunkMapperModel.pretrained("abbreviation_category_mapper", "en", "clinical/models")\
+    .setInputCols(["abbr_ner_chunk"])\
+    .setOutputCol("mappings")\
+    .setRels(["definition", "category"])\
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    sentence_detector,
+    tokenizer, 
+    word_embeddings,
+    abbr_ner, 
+    abbr_converter, 
+    chunkerMapper])
+
+
+text = ["""Gravid with estimated fetal weight of 6-6/12 pounds.
+           LABORATORY DATA: Laboratory tests include a CBC which is normal. 
+           VDRL: Nonreactive
+           HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."""]
+
+data = spark.createDataFrame([text]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val sentence_detector = new SentenceDetector()
+    .setInputCols(Array("document"))
+    .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("sentence")
+    .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
+    .setInputCols(Array("sentence", "token"))
+    .setOutputCol("embeddings")
+
+val abbr_ner = MedicalNerModel.pretrained("ner_abbreviation_clinical", "en", "clinical/models") 
+    .setInputCols(Array("sentence", "token", "embeddings")) 
+    .setOutputCol("abbr_ner")
+
+val abbr_converter = new NerConverter() 
+    .setInputCols(Array("sentence", "token", "abbr_ner")) 
+    .setOutputCol("abbr_ner_chunk")
+
+val chunkerMapper = ChunkMapperModel.pretrained("abbreviation_category_mapper", "en", "clinical/models")
+    .setInputCols("abbr_ner_chunk")
+    .setOutputCol("mappings")
+    .setRels(Array("definition", "category"))
+
+
+val pipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer, 
+    word_embeddings,
+    abbr_ner, 
+    abbr_converter, 
+    chunkerMapper))
+
+
+val sample_text = """Gravid with estimated fetal weight of 6-6/12 pounds.
+           LABORATORY DATA: Laboratory tests include a CBC which is normal. 
+           VDRL: Nonreactive
+           HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""" 
+
+
+val data = Seq(sample_text).toDS.toDF("text")
+
+val result= pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
+|    | chunk   | category          | definition                             |
+|---:|:--------|:------------------|:---------------------------------------|
+|  0 | CBC     | general           | complete blood count                   |
+|  1 | VDRL    | clinical_dept     | Venereal Disease Research Laboratories |
+|  2 | HIV     | medical_condition | Human immunodeficiency virus           |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|abbreviation_category_mapper|
+|Compatibility:|Healthcare NLP 4.2.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[abbr_ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|128.2 KB|