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.../_posts/Meryem1425/2025-01-16-clinical_deidentification_docwise_benchmark_en.md
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| --- | ||
| layout: model | ||
| title: Clinical Deidentification Pipeline (Document Wise - Benchmark) | ||
| author: John Snow Labs | ||
| name: clinical_deidentification_docwise_benchmark | ||
| date: 2025-01-16 | ||
| tags: [licensed, en, deidentification, deid, pipeline, clinical, docwise, benchmark] | ||
| task: [De-identification, Pipeline Healthcare] | ||
| language: en | ||
| edition: Healthcare NLP 5.5.1 | ||
| spark_version: 3.4 | ||
| supported: true | ||
| annotator: PipelineModel | ||
| article_header: | ||
| type: cover | ||
| use_language_switcher: "Python-Scala-Java" | ||
| --- | ||
|
|
||
| ## Description | ||
|
|
||
| This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate `NAME`, `IDNUM`, `CONTACT`, `LOCATION`, `AGE`, `DATE` entities. | ||
| **This pipeline is prepared for benchmarking with cloud providers.** | ||
|
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| ## Predicted Entities | ||
|
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| `NAME`, `IDNUM`, `CONTACT`, `LOCATION`, `AGE`, `DATE` | ||
|
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||
| {:.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/clinical_deidentification_docwise_benchmark_en_5.5.1_3.4_1737046494582.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} | ||
| [Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_benchmark_en_5.5.1_3.4_1737046494582.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} | ||
|
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| ## How to use | ||
|
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||
|
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||
|
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| <div class="tabs-box" markdown="1"> | ||
| {% include programmingLanguageSelectScalaPythonNLU.html %} | ||
|
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| ```python | ||
|
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| from sparknlp.pretrained import PretrainedPipeline | ||
|
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| deid_pipeline = PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
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| deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
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||
| print(''.join([i.result for i in deid_result['mask_entity']])) | ||
| print(''.join([i.result for i in deid_result['obfuscated']])) | ||
|
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| ``` | ||
|
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| {:.jsl-block} | ||
| ```python | ||
|
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| deid_pipeline = nlp.PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
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| deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
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||
| print(''.join([i.result for i in deid_result['mask_entity']])) | ||
| print(''.join([i.result for i in deid_result['obfuscated']])) | ||
|
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||
| ``` | ||
| ```scala | ||
|
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| import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline | ||
|
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||
| val deid_pipeline = PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
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| val deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
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||
| println(deid_result("mask_entity").map(_("result").toString).mkString("")) | ||
| println(deid_result("obfuscated").map(_("result").toString).mkString("")) | ||
|
|
||
| ``` | ||
| </div> | ||
|
|
||
| ## Results | ||
|
|
||
| ```bash | ||
|
|
||
| Masked with entity labels | ||
| ------------------------------ | ||
| Name : <NAME>, Record date: <DATE>, # <IDNUM>. | ||
| Dr. <NAME>, ID: <IDNUM>, IP <IDNUM>. | ||
| He is a <AGE> male was admitted to the <LOCATION> for cystectomy on <DATE>. | ||
| Patient's VIN : <IDNUM>, SSN <IDNUM>, Driver's license <IDNUM>. | ||
| Phone <CONTACT>, <LOCATION>, <LOCATION>, E-MAIL: <CONTACT>. | ||
|
|
||
|
|
||
| Obfuscated | ||
| ------------------------------ | ||
| Name : Lawrnce Pretzel, Record date: 2093-01-24, # 486302. | ||
| Dr. Carolina Cid, ID: 5875955427, IP 089.708.009.79. | ||
| He is a 65-year-old male was admitted to the South Benjaminside for cystectomy on 01/24/93. | ||
| Patient's VIN : 0OZUO50MYTQ018397, SSN #888-11-3333, Driver's license YZ:Z881100W. | ||
| Phone (546) 920-7669, Traceyburgh, 1441 Eastlake Avenue, E-MAIL: [email protected]. | ||
|
|
||
| ``` | ||
|
|
||
| {:.model-param} | ||
| ## Model Information | ||
|
|
||
| {:.table-model} | ||
| |---|---| | ||
| |Model Name:|clinical_deidentification_docwise_benchmark| | ||
| |Type:|pipeline| | ||
| |Compatibility:|Healthcare NLP 5.5.1+| | ||
| |License:|Licensed| | ||
| |Edition:|Official| | ||
| |Language:|en| | ||
| |Size:|2.5 GB| | ||
|
|
||
| ## Included Models | ||
|
|
||
| - DocumentAssembler | ||
| - InternalDocumentSplitter | ||
| - TokenizerModel | ||
| - WordEmbeddingsModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - MedicalNerModel | ||
| - MedicalNerModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - NerConverterInternalModel | ||
| - NerConverterInternalModel | ||
| - PretrainedZeroShotNER | ||
| - NerConverterInternalModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - ContextualEntityRuler | ||
| - ChunkMergeModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - TextMatcherInternalModel | ||
| - TextMatcherInternalModel | ||
| - ContextualParserModel | ||
| - RegexMatcherInternalModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - RegexMatcherInternalModel | ||
| - RegexMatcherInternalModel | ||
| - ChunkMergeModel | ||
| - ChunkMergeModel | ||
| - LightDeIdentification | ||
| - LightDeIdentification |
166 changes: 166 additions & 0 deletions
166
docs/_posts/akrztrk/2025-01-16-clinical_deidentification_docwise_benchmark_en.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| --- | ||
| layout: model | ||
| title: Clinical Deidentification Pipeline (Document Wise - Benchmark) | ||
| author: John Snow Labs | ||
| name: clinical_deidentification_docwise_benchmark | ||
| date: 2025-01-16 | ||
| tags: [licensed, en, deidentification, deid, pipeline, clinical, docwise, benchmark] | ||
| task: [De-identification, Pipeline Healthcare] | ||
| language: en | ||
| edition: Healthcare NLP 5.5.1 | ||
| spark_version: 3.2 | ||
| supported: true | ||
| annotator: PipelineModel | ||
| article_header: | ||
| type: cover | ||
| use_language_switcher: "Python-Scala-Java" | ||
| --- | ||
|
|
||
| ## Description | ||
|
|
||
| This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate `NAME`, `IDNUM`, `CONTACT`, `LOCATION`, `AGE`, `DATE` entities. | ||
| **This pipeline is prepared for benchmarking with cloud providers.** | ||
|
|
||
| ## Predicted Entities | ||
|
|
||
| `NAME`, `IDNUM`, `CONTACT`, `LOCATION`, `AGE`, `DATE` | ||
|
|
||
| {:.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/clinical_deidentification_docwise_benchmark_en_5.5.1_3.2_1737048679338.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} | ||
| [Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_benchmark_en_5.5.1_3.2_1737048679338.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 | ||
|
|
||
| deid_pipeline = PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
|
||
| deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
|
||
| print(''.join([i.result for i in deid_result['mask_entity']])) | ||
| print(''.join([i.result for i in deid_result['obfuscated']])) | ||
|
|
||
| ``` | ||
|
|
||
| {:.jsl-block} | ||
| ```python | ||
|
|
||
| deid_pipeline = nlp.PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
|
||
| deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
|
||
| print(''.join([i.result for i in deid_result['mask_entity']])) | ||
| print(''.join([i.result for i in deid_result['obfuscated']])) | ||
|
|
||
| ``` | ||
| ```scala | ||
|
|
||
| import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline | ||
|
|
||
| val deid_pipeline = PretrainedPipeline("clinical_deidentification_docwise_benchmark", "en", "clinical/models") | ||
|
|
||
| val deid_result = deid_pipeline.fullAnnotate("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. | ||
| Dr. John Green, ID: 1231511863, IP 203.120.223.13. | ||
| He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. | ||
| Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. | ||
| Phone (302) 786-5227, Keats Street, San Francisco, E-MAIL: [email protected].""") | ||
|
|
||
| println(deid_result("mask_entity").map(_("result").toString).mkString("")) | ||
| println(deid_result("obfuscated").map(_("result").toString).mkString("")) | ||
|
|
||
| ``` | ||
| </div> | ||
|
|
||
| ## Results | ||
|
|
||
| ```bash | ||
|
|
||
| Masked with entity labels | ||
| ------------------------------ | ||
| Name : <NAME>, Record date: <DATE>, # <IDNUM>. | ||
| Dr. <NAME>, ID: <IDNUM>, IP <IDNUM>. | ||
| He is a <AGE> male was admitted to the <LOCATION> for cystectomy on <DATE>. | ||
| Patient's VIN : <IDNUM>, SSN <IDNUM>, Driver's license <IDNUM>. | ||
| Phone <CONTACT>, <LOCATION>, <LOCATION>, E-MAIL: <CONTACT>. | ||
|
|
||
|
|
||
| Obfuscated | ||
| ------------------------------ | ||
| Name : Laray Platt, Record date: 2093-02-17, # 264180. | ||
| Dr. Tedd Favorite, ID: 1431511083, IP 534.253.554.24. | ||
| He is a 71-year-old male was admitted to the 900 Hospital Drive for cystectomy on 02/17/93. | ||
| Patient's VIN : 7HSNH27FRMJ785064, SSN #999-22-4444, Driver's license RS:S114433P. | ||
| Phone (546) 920-7669, 830 Kempsville Road, 624 N Second, E-MAIL: [email protected]. | ||
|
|
||
| ``` | ||
|
|
||
| {:.model-param} | ||
| ## Model Information | ||
|
|
||
| {:.table-model} | ||
| |---|---| | ||
| |Model Name:|clinical_deidentification_docwise_benchmark| | ||
| |Type:|pipeline| | ||
| |Compatibility:|Healthcare NLP 5.5.1+| | ||
| |License:|Licensed| | ||
| |Edition:|Official| | ||
| |Language:|en| | ||
| |Size:|2.5 GB| | ||
|
|
||
| ## Included Models | ||
|
|
||
| - DocumentAssembler | ||
| - InternalDocumentSplitter | ||
| - TokenizerModel | ||
| - WordEmbeddingsModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - MedicalNerModel | ||
| - MedicalNerModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - NerConverterInternalModel | ||
| - NerConverterInternalModel | ||
| - PretrainedZeroShotNER | ||
| - NerConverterInternalModel | ||
| - MedicalNerModel | ||
| - NerConverterInternalModel | ||
| - ContextualEntityRuler | ||
| - ChunkMergeModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - TextMatcherInternalModel | ||
| - TextMatcherInternalModel | ||
| - ContextualParserModel | ||
| - RegexMatcherInternalModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - ContextualParserModel | ||
| - RegexMatcherInternalModel | ||
| - RegexMatcherInternalModel | ||
| - ChunkMergeModel | ||
| - ChunkMergeModel | ||
| - LightDeIdentification | ||
| - LightDeIdentification |
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