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2024-06-25-explain_clinical_doc_radiology_en (#1286)
* Add model 2024-06-25-explain_clinical_doc_radiology_en * Update 2024-06-25-explain_clinical_doc_radiology_en.md --------- Co-authored-by: bugeki <[email protected]> Co-authored-by: bugra <[email protected]>
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‎docs/_posts/bugeki/2024-06-25-explain_clinical_doc_radiology_en.md

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---
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layout: model
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title: Explain Clinical Document - Radiology
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author: John Snow Labs
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name: explain_clinical_doc_radiology
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date: 2024-06-25
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tags: [licensed, en, radiology, ner, pipeline, posology, assertion, relation_extraction]
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task: [Pipeline Healthcare, Named Entity Recognition, Assertion Status, Relation Extraction]
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language: en
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edition: Healthcare NLP 5.3.3
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spark_version: 3.0
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supported: true
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annotator: PipelineModel
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article_header:
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type: cover
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use_language_switcher: "Python-Scala-Java"
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---
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## Description
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This specialized radiology pipeline can;
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- extract radiology related entities,
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- assign assertion status to the extracted entities,
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- establish relations between the extracted entities from the clinical documents.
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In this pipeline, five NER, one assertion and one relation extraction model were used to achive those tasks.
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- Clinical Entity Labels: `Imaging_Test`, `Imaging_Technique`, `ImagingFindings`, `OtherFindings`, `BodyPart`, `Direction`, `Test`, `Symptom`, `Disease_Syndrome_Disorder`, `Medical_Device`, `Procedure`, `Measurements`, `Units`, `Gender`, `Metastasis`, `Invasion`, `Route`, `Treatment`, `Drug`, `Form`, `Frequency`, `Dosage`, `Date`, `Test_Result`
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- Assertion Status Labels: `Confirmed`, `Suspected`, `Negative`
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- Relation Extraction Labels: `is_related`, `not_related`
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{:.btn-box}
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<button class="button button-orange" disabled>Live Demo</button>
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<button class="button button-orange" disabled>Open in Colab</button>
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[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/explain_clinical_doc_radiology_en_5.3.3_3.0_1719318745883.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/explain_clinical_doc_radiology_en_5.3.3_3.0_1719318745883.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">
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{% include programmingLanguageSelectScalaPythonNLU.html %}
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```python
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from sparknlp.pretrained import PretrainedPipeline
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ner_pipeline = PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
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result = ner_pipeline.annotate("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter
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located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow.
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This may represent benign fibrous tissue or a lipoma.""")
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```
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```scala
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import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
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val ner_pipeline = PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
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val result = ner_pipeline.annotate("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter
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located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow.
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This may represent benign fibrous tissue or a lipoma.""")
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```
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</div>
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## Results
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```bash
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# NER Result
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+----------------------------------------+-----+---+-------------------------+
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|ner_chunk |begin|end|ner_label |
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+----------------------------------------+-----+---+-------------------------+
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|Bilateral breast |0 |15 |BodyPart |
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|ultrasound |17 |26 |Imaging_Test |
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|ovoid mass |78 |87 |ImagingFindings |
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|0.5 x 0.5 x 0.4 cm |113 |130|Measurements |
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|anteromedial aspect of the left shoulder|163 |202|BodyPart |
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|mass |210 |213|ImagingFindings |
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|isoechoic echotexture |228 |248|ImagingFindings |
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|adjacent muscle |257 |271|BodyPart |
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|internal color flow |294 |312|ImagingFindings |
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|benign fibrous tissue |334 |354|ImagingFindings |
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|lipoma |361 |366|Disease_Syndrome_Disorder|
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+----------------------------------------+-----+---+-------------------------+
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# Assertion Result
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+---------------------+-----+---+-------------------------+---------+-----------+
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|chunks |begin|end|entities |assertion|confidence)|
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+---------------------+-----+---+-------------------------+---------+-----------+
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|ovoid mass |78 |87 |ImagingFindings |Confirmed|0.9966 |
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|mass |210 |213|ImagingFindings |Confirmed|0.9683 |
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|isoechoic echotexture|228 |248|ImagingFindings |Confirmed|0.9932 |
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|internal color flow |294 |312|ImagingFindings |Negative |0.9632 |
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|benign fibrous tissue|334 |354|ImagingFindings |Suspected|0.9951 |
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|lipoma |361 |366|Disease_Syndrome_Disorder|Suspected|0.9676 |
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+---------------------+-----+---+-------------------------+---------+-----------+
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# Relation Extraction Result
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+------------+-----------------+---------------+-----------------------+-----------------+-----------------+---------------+--------------------+-----------------+---------------------------+
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| sentence | entity1_begin | entity1_end | chunk1 | entity1 | entity2_begin | entity2_end | chunk2 | entity2 | relation | confidence |
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|-----------:|----------------:|--------------:|:----------------------|:----------------|----------------:|--------------:|:-------------------|:----------------|:-----------|-------------:|
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| 0 | 0 | 15 | Bilateral breast | BodyPart | 17 | 26 | ultrasound | Imaging_Test | is_related | 1 |
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| 0 | 0 | 15 | Bilateral breast | BodyPart | 78 | 87 | ovoid mass | ImagingFindings | is_related | 0.999997 |
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| 0 | 17 | 26 | ultrasound | Imaging_Test | 78 | 87 | ovoid mass | ImagingFindings | is_related | 0.999569 |
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| 0 | 78 | 87 | ovoid mass | ImagingFindings | 113 | 130 | 0.5 x 0.5 x 0.4 cm | Measurements | is_related | 1 |
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| 1 | 210 | 213 | mass | ImagingFindings | 257 | 271 | adjacent muscle | BodyPart | is_related | 0.997639 |
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| 1 | 228 | 248 | isoechoic echotexture | ImagingFindings | 257 | 271 | adjacent muscle | BodyPart | is_related | 0.999999 |
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+------------+-----------------+---------------+-----------------------+-----------------+-----------------+---------------+--------------------+-----------------+---------------------------+
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```
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{:.model-param}
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## Model Information
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{:.table-model}
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|---|---|
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|Model Name:|explain_clinical_doc_radiology|
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|Type:|pipeline|
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|Compatibility:|Healthcare NLP 5.3.3+|
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|License:|Licensed|
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|Edition:|Official|
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|Language:|en|
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|Size:|1.8 GB|
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## Included Models
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- DocumentAssembler
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- SentenceDetectorDLModel
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- TokenizerModel
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- WordEmbeddingsModel
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- MedicalNerModel
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- NerConverterInternalModel
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- MedicalNerModel
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- NerConverterInternalModel
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- MedicalNerModel
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- NerConverterInternalModel
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- MedicalNerModel
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- NerConverterInternalModel
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- MedicalNerModel
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- NerConverterInternalModel
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- ChunkMergeModel
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- ChunkMergeModel
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- AssertionDLModel
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- PerceptronModel
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- DependencyParserModel
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- RelationExtractionModel

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