2024-04-13-ner_deid_large_pipe_en

docs/_posts/gadde5300/2024-04-13-ner_deid_large_pipe_en.md

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+---
+layout: model
+title: Image De-Identification Pipeline
+author: John Snow Labs
+name: ner_deid_large_pipe
+date: 2024-04-13
+tags: [en, licensed, ocr, image, deid, pipeline]
+task: Pipeline Healthcare
+language: en
+edition: Healthcare NLP 5.3.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Deidentification NER (Large) is a Named Entity Recognition model that annotates text to find protected health information that may need to be deidentified. The entities it annotates are Age, Contact, Date, Id, Location, Name, and Profession. This model is trained with the embeddings_clinical word embeddings model, so be sure to use the same embeddings in the pipeline.
+
+It protects specific health information that could identify living or deceased individuals. The rule preserves patient confidentiality without affecting the values and the information that could be needed for different research purposes.
+
+{:.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_deid_large_pipe_en_5.3.1_3.0_1713038644675.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_deid_large_pipe_en_5.3.1_3.0_1713038644675.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
+
+nlu_model = PretrainedPipeline("ner_deid_large_pipe", "en", "clinical/models")
+
+image_path = visual.pkg_resources.resource_filename('sparkocr', 'resources/ocr/images/p1.jpg')
+image_df = spark.read.format("binaryFile").load(image_path)
+
+result = nlu_model.transform(image_df)
+```
+
+</div>
+
+## Results
+
+```bash
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|ner_chunk                                                                                                                                                                                                                                                                                                   |
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[{chunk, 193, 202, 04/04/2018, {entity -> DATE, sentence -> 1, chunk -> 0, confidence -> 0.9999}, []}, {chunk, 3290, 3290, ., {entity -> NAME, sentence -> 17, chunk -> 1, confidence -> 0.6035}, []}, {chunk, 3388, 3397, 04/12/2018, {entity -> DATE, sentence -> 20, chunk -> 2, confidence -> 1.0}, []}]|
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_deid_large_pipe|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.3.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.7 GB|
+
+## Included Models
+
+- BinaryToImage
+- ImageToText
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter