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* Add model 2024-03-26-meddra_pt_resolver_pipeline_en * Update 2024-03-26-meddra_pt_resolver_pipeline_en.md * Add model 2024-03-26-meddra_llt_resolver_pipeline_en * Update 2024-03-26-meddra_llt_resolver_pipeline_en.md * Update 2024-03-26-meddra_pt_resolver_pipeline_en.md * Update 2024-03-26-meddra_llt_resolver_pipeline_en.md * Update 2024-03-26-meddra_pt_resolver_pipeline_en.md --------- Co-authored-by: Meryem1425 <[email protected]> Co-authored-by: Vildan <[email protected]> Co-authored-by: muhammetsnts <[email protected]>
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‎docs/_posts/Meryem1425/2024-03-26-meddra_llt_resolver_pipeline_en.md

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---
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layout: model
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title: Pipeline for MedDRA - Lowest Level Term (LLT) Sentence Entity Resolver
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author: John Snow Labs
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name: meddra_llt_resolver_pipeline
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date: 2024-03-26
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tags: [licensed, en, meddra, llt, resolver]
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task: Entity Resolution
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language: en
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edition: Healthcare NLP 5.3.1
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spark_version: 3.4
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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 dedicated pipeline extracts clinical terms and utilizes `sbiobert_base_cased_mli` Sentence Bert Embeddings to link them to their corresponding MedDRA LLT (Lowest Level Term) codes. Additionally, it provides the MedDRA Preferred Term codes for each MedDRA LLT code within the `meddra_llt_code` metadata's `all_k_aux_labels`. Furthermore, it conducts mappings of MedDRA LLT codes to MedDRA Preferred Term (PT) codes using the `meddra_llt_pt_mapper` model and to ICD-10 codes using the `meddra_llt_icd10_mapper` model.
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This pipeline can extract the following clincial entities: `Procedure`, `Kidney_Disease`, `Cerebrovascular_Disease`, `Heart_Disease`, `Disease_Syndrome_Disorder`, `ImagingFindings`, `Symptom`, `VS_Finding`, `EKG_Findings`, `Communicable_Disease`, `Substance`, `Internal_organ_or_component`, `External_body_part_or_region`, `Modifier`, `Triglycerides`, `Alcohol`, `Smoking`, `Pregnancy`, `Hypertension`, `Obesity`, `Injury_or_Poisoning`, `Test`, `Hyperlipidemia`, `BMI`, `Oncological`, `Psychological_Condition`, `LDL`, `Diabetes`, `PROBLEM`.
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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/meddra_llt_resolver_pipeline_en_5.3.1_3.4_1711474115662.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/meddra_llt_resolver_pipeline_en_5.3.1_3.4_1711474115662.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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meddra_llt_pipeline = PretrainedPipeline.from_disk("meddra_llt_resolver_pipeline")
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result = meddra_llt_pipeline.fullAnnotate('This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, chronic obstructive pulmonary disease, gastritis, and transient ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia.')
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```
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```scala
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import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
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val meddra_llt_pipeline = PretrainedPipeline.from_disk("meddra_llt_resolver_pipeline")
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val result = meddra_llt_pipeline.fullAnnotate("This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, chronic obstructive pulmonary disease, gastritis, and transient ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia.")
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```
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</div>
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## Results
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```bash
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+--------------------------------------+-------------------------+---------------+-------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------+
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|chunk |label |meddra_llt_code|resolution |icd10_mappings |meddra_pt_mappings |
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+--------------------------------------+-------------------------+---------------+-------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------+
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|tobacco |Smoking |10067622 |tobacco interaction |NONE |10067622:Tobacco interaction |
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|hypertension |Hypertension |10020772 |hypertension |O10:Pre-existing hypertension complicating pregnancy, childbirth and the puerperium|10020772:Hypertension |
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|chronic renal insufficiency |Kidney_Disease |10050441 |chronic renal insufficiency |NONE |10064848:Chronic kidney disease |
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|chronic obstructive pulmonary disease |Disease_Syndrome_Disorder|10009033 |chronic obstructive pulmonary disease|J44:Other chronic obstructive pulmonary disease |10009033:Chronic obstructive pulmonary disease |
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|gastritis |Disease_Syndrome_Disorder|10017853 |gastritis |K29.6:Other gastritis |10017853:Gastritis |
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|transient ischemic attack |Cerebrovascular_Disease |10072760 |transient ischemic attack |NONE |10044390:Transient ischaemic attack |
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|ST elevation |PROBLEM |10041887 |st elevated |NONE |10014392:Electrocardiogram ST segment elevation|
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|cardiac catheterization |Procedure |10048606 |cardiac catheterization |Y84.0:Cardiac catheterization |10007815:Catheterisation cardiac |
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|the left main coronary artery stenosis|PROBLEM |10090240 |left main coronary artery stenosis |NONE |10011089:Coronary artery stenosis |
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|hypotension |VS_Finding |10021097 |hypotension |I95:Hypotension |10021097:Hypotension |
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|bradycardia |VS_Finding |10006093 |bradycardia |R00.1:Bradycardia, unspecified |10006093:Bradycardia |
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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:|meddra_llt_resolver_pipeline|
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|Type:|pipeline|
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|Compatibility:|Healthcare NLP 5.3.1+|
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|License:|Licensed|
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|Edition:|Official|
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|Language:|en|
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|Size:|2.4 GB|
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## References
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This pipeline is prepared using the models that are trained with the January 2024 release (v27) of MedDRA dataset.
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**To utilize this pipeline, possession of a valid MedDRA license is requisite. If you possess one and wish to use this model, kindly contact us at [email protected].**
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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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- ChunkMergeModel
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- Chunk2Doc
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- BertSentenceEmbeddings
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- SentenceEntityResolverModel
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- Resolution2Chunk
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- ChunkMapperModel
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- ChunkMapperModel

‎docs/_posts/Meryem1425/2024-03-26-meddra_pt_resolver_pipeline_en.md

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---
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layout: model
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title: Pipeline for MedDRA - Preferred Term (PT) Sentence Entity Resolver
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author: John Snow Labs
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name: meddra_pt_resolver_pipeline
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date: 2024-03-26
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tags: [licensed, en, meddra, resolver, pt]
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task: Entity Resolution
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language: en
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edition: Healthcare NLP 5.3.1
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spark_version: 3.4
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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 dedicated pipeline extracts clinical terms and utilizes `sbiobert_base_cased_mli` Sentence Bert Embeddings to link them to their corresponding MedDRA PT (Preferred Term) codes. Additionally, it provides the MedDRA Preferred Term codes for each MedDRA PT code within the `meddra_pt_code` metadata's `all_k_aux_labels`. Furthermore, it conducts mappings of MedDRA PT codes to MedDRA Lowest Level Term (LLT) codes using the `meddra_pt_llt_mapper` model and to ICD-10 codes using the `meddra_pt_icd10_mapper` model.
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This pipeline can extract the following clincial entities: `Procedure`, `Kidney_Disease`, `Cerebrovascular_Disease`, `Heart_Disease`, `Disease_Syndrome_Disorder`, `ImagingFindings`, `Symptom`, `VS_Finding`, `EKG_Findings`, `Communicable_Disease`, `Substance`, `Internal_organ_or_component`, `External_body_part_or_region`, `Modifier`, `Triglycerides`, `Alcohol`, `Smoking`, `Pregnancy`, `Hypertension`, `Obesity`, `Injury_or_Poisoning`, `Test`, `Hyperlipidemia`, `BMI`, `Oncological`, `Psychological_Condition`, `LDL`, `Diabetes`, `PROBLEM`.
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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/meddra_pt_resolver_pipeline_en_5.3.1_3.4_1711469950015.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/meddra_pt_resolver_pipeline_en_5.3.1_3.4_1711469950015.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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meddra_pt_pipeline = PretrainedPipeline.from_disk("meddra_pt_resolver_pipeline")
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result = meddra_pt_pipeline.fullAnnotate('This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, chronic obstructive pulmonary disease, gastritis, and transient ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia.')
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```
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```scala
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import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
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val meddra_pt_pipeline = PretrainedPipeline.from_disk("meddra_pt_resolver_pipeline")
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val result = meddra_pt_pipeline.fullAnnotate("This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, chronic obstructive pulmonary disease, gastritis, and transient ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia.")
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```
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</div>
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## Results
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```bash
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+--------------------------------------+-------------------------+--------------+-------------------------------------+-----------------------------------------------------------------------------------+-------------------------------------------+
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|chunk |label |meddra_pt_code|resolution |icd10_mappings |meddra_llt_mappings |
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+--------------------------------------+-------------------------+--------------+-------------------------------------+-----------------------------------------------------------------------------------+-------------------------------------------+
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|tobacco |Smoking |10067622 |tobacco interaction |NONE |10067622:Tobacco interaction |
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|hypertension |Hypertension |10020772 |hypertension |O10:Pre-existing hypertension complicating pregnancy, childbirth and the puerperium|10005747:Blood pressure high |
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|chronic renal insufficiency |Kidney_Disease |10038435 |renal failure |N19:Unspecified kidney failure |10016149:Failure kidney |
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|chronic obstructive pulmonary disease |Disease_Syndrome_Disorder|10009033 |chronic obstructive pulmonary disease|J44:Other chronic obstructive pulmonary disease |10008828:Chronic airflow limitation |
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|gastritis |Disease_Syndrome_Disorder|10017853 |gastritis |K29:Gastritis and duodenitis |10000769:Acute gastritis |
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|transient ischemic attack |Cerebrovascular_Disease |10044390 |transient ischaemic attack |G45:Transient cerebral ischaemic attacks and related syndromes |10022561:Intermittent cerebral claudication|
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|ST elevation |PROBLEM |10049785 |atrial pressure increased |NONE |10049785:Atrial pressure increased |
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|cardiac catheterization |Procedure |10007815 |catheterisation cardiac |Y84.0:Cardiac catheterization |10007527:Cardiac catheterisation |
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|the left main coronary artery stenosis|PROBLEM |10011089 |coronary artery stenosis |NONE |10011089:Coronary artery stenosis |
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|hypotension |VS_Finding |10021097 |hypotension |I95:Hypotension |10005753:Blood pressure low |
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|bradycardia |VS_Finding |10006093 |bradycardia |R00.1:Bradycardia, unspecified |10006093:Bradycardia |
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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:|meddra_pt_resolver_pipeline|
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|Type:|pipeline|
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|Compatibility:|Healthcare NLP 5.3.1+|
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|License:|Licensed|
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|Edition:|Official|
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|Language:|en|
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|Size:|2.2 GB|
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## References
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This pipeline is prepared using the models that are trained with the January 2024 release (v27) of MedDRA dataset.
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**To utilize this pipeline, possession of a valid MedDRA license is requisite. If you possess one and wish to use this model, kindly contact us at [email protected].**
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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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- ChunkMergeModel
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- Chunk2Doc
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- BertSentenceEmbeddings
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- SentenceEntityResolverModel
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- Resolution2Chunk
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- ChunkMapperModel
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- ChunkMapperModel

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