2024-03-03-snomed_findings_resolver_pipeline_en

docs/_posts/akrztrk/2024-03-03-snomed_findings_resolver_pipeline_en.md

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+---
+layout: model
+title: PiPipeline for Snomed Concept, Findings Version
+author: John Snow Labs
+name: snomed_findings_resolver_pipeline
+date: 2024-03-03
+tags: [licensed, en, snomed, pipeline, resolver]
+task: [Entity Resolution, Pipeline Healthcare]
+language: en
+edition: Healthcare NLP 5.3.0
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pipeline extracts clinical findings and maps them to their corresponding SNOMED (CT version) codes using `sbiobert_base_cased_mli` Sentence Bert Embeddings.
+
+{:.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/snomed_findings_resolver_pipeline_en_5.3.0_3.4_1709489839090.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_findings_resolver_pipeline_en_5.3.0_3.4_1709489839090.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
+
+snomed_pipeline = PretrainedPipeline("snomed_findings_resolver_pipeline", "en", "clinical/models")
+
+result = snomed_pipeline.annotate("""The patient exhibited recurrent upper respiratory tract infections, fever, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia.""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val snomed_pipeline = PretrainedPipeline("snomed_findings_resolver_pipeline", "en", "clinical/models")
+
+val result = snomed_pipeline.annotate("""The patient exhibited recurrent upper respiratory tract infections, fever, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia.""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|                             chunk|begin|end|ner_label|snomed_code|                                 resolution|                                           all_k_resolutions|                                                 all_k_codes|
++----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|upper respiratory tract infections|   32| 65|  PROBLEM|  195708003|recurrent upper respiratory tract infection|recurrent upper respiratory tract infection:::upper respi...|195708003:::54150009:::312118003:::448739000:::4519910001...|
+|                             fever|   68| 72|  PROBLEM|  386661006|                                      fever|fever:::intermittent fever:::sustained fever:::prolonged ...|386661006:::77957000:::271751000:::248435007:::12579009::...|
+|         unintentional weight loss|   75| 99|  PROBLEM|  448765001|                  unintentional weight loss|unintentional weight loss:::unexplained weight loss:::int...|448765001:::422868009:::416528001:::267024001:::89362005:...|
+|                      night sweats|  117|128|  PROBLEM|   42984000|                               night sweats|night sweats:::frequent night waking:::night waking:::nig...|42984000:::423052008:::67233009:::102549009:::36163009:::...|
+|                         cachectic|  157|165|  PROBLEM|  238108007|                                  cachectic|cachectic:::cachexia associated with aids:::cardiac cache...|238108007:::422003001:::284529003:::788876001:::240128005...|
+|                              pale|  171|174|  PROBLEM|  398979000|                            pale complexion|pale complexion:::pale liver:::pale tongue:::pale lung:::...|398979000:::95199009:::719637000:::95200007:::70396004:::...|
+|                hepatosplenomegaly|  190|207|  PROBLEM|   36760000|                         hepatosplenomegaly|hepatosplenomegaly:::congestive splenomegaly:::neonatal h...|36760000:::19058002:::80378000:::16294009:::191382009:::8...|
+|                      pancytopenia|  239|250|  PROBLEM|  127034005|                               pancytopenia|pancytopenia:::drug induced pancytopenia:::pancytopenia -...|127034005:::736024007:::5876000:::124961001:::417672002::...|
++----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|snomed_findings_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.3.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|2.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- ChunkMergeModel
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel

docs/_posts/akrztrk/2024-03-04-sbiobertresolve_snomed_bodyStructure_en.md

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+---
+layout: model
+title: Sentence Entity Resolver for SNOMED (sbiobertresolve_snomed_bodyStructure)
+author: John Snow Labs
+name: sbiobertresolve_snomed_bodyStructure
+date: 2024-03-04
+tags: [licensed, en, resolver, snomed, bodystructure]
+task: Entity Resolution
+language: en
+edition: Healthcare NLP 5.3.0
+spark_version: 3.0
+supported: true
+annotator: SentenceEntityResolverModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model maps extracted medical (anatomical structure) entities to SNOMED codes (body structure version) using `sbiobert_base_cased_mli` BERT sentence embeddings
+
+## Predicted Entities
+
+
+
+{:.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/sbiobertresolve_snomed_bodyStructure_en_5.3.0_3.0_1709543980434.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/sbiobertresolve_snomed_bodyStructure_en_5.3.0_3.0_1709543980434.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
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence")
+
+tokenizer = Tokenizer()\
+    .setInputCols(["sentence"])\
+    .setOutputCol("token")\
+
+word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
+    .setInputCols(["sentence", "token"])\
+    .setOutputCol("embeddings")
+
+ner_jsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models") \
+    .setInputCols(["sentence", "token", "embeddings"]) \
+    .setOutputCol("ner_jsl")
+
+ner_jsl_converter = NerConverterInternal() \
+    .setInputCols(["sentence", "token", "ner_jsl"]) \
+    .setOutputCol("ner_jsl_chunk")\
+    .setWhiteList(["External_body_part_or_region", 
+                   "Internal_organ_or_component"])\
+    .setReplaceLabels({"External_body_part_or_region": "BodyPart", 
+                       "Internal_organ_or_component": "BodyPart" })
+
+ner_anatomy = MedicalNerModel.pretrained("ner_anatomy_coarse", "en", "clinical/models") \
+    .setInputCols(["sentence", "token", "embeddings"]) \
+    .setOutputCol("ner_anatomy")
+
+ner_anatomy_converter = NerConverterInternal() \
+    .setInputCols(["sentence", "token", "ner_anatomy"]) \
+    .setOutputCol("ner_anatomy_chunk")\
+    .setReplaceLabels({"Anatomy": "BodyPart"})
+
+ner_oncology_anatomy = MedicalNerModel.pretrained("ner_oncology_anatomy_general", "en", "clinical/models") \
+    .setInputCols(["sentence", "token", "embeddings"]) \
+    .setOutputCol("ner_oncology_anatomy")
+
+ner_oncology_anatomy_converter = NerConverterInternal() \
+    .setInputCols(["sentence", "token", "ner_oncology_anatomy"]) \
+    .setOutputCol("ner_oncology_anatomy_chunk")\
+    .setReplaceLabels({"Anatomical_Site": "BodyPart"})
+
+chunk_merger = ChunkMergeApproach() \
+    .setInputCols("ner_jsl_chunk", "ner_anatomy_chunk", "ner_oncology_anatomy_chunk") \
+    .setOutputCol("ner_chunk") \
+
+chunk2doc = Chunk2Doc()\
+    .setInputCols("ner_chunk")\
+    .setOutputCol("ner_chunk_doc")
+
+sbert_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
+    .setInputCols(["ner_chunk_doc"])\
+    .setOutputCol("sbert_embeddings")\
+    .setCaseSensitive(False)
+
+snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_bodyStructure", "en", "clinical/models") \
+    .setInputCols(["sbert_embeddings"]) \
+    .setOutputCol("snomed_code")\
+
+snomed_pipeline = Pipeline(stages = [
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    word_embeddings,
+    ner_jsl,
+    ner_jsl_converter,
+    ner_anatomy,
+    ner_anatomy_converter,
+    ner_oncology_anatomy,
+    ner_oncology_anatomy_converter,
+    chunk_merger,
+    chunk2doc,
+    sbert_embeddings,
+    snomed_resolver
+])
+
+
+data = spark.createDataFrame([["""The patient is a 30-year-old female with a long history of insulin-dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI."""]]).toDF("text")
+
+model = snomed_pipeline.fit(data)
+result = model.transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+    .setInputCols(Array("document"))
+    .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("sentence"))
+    .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
+    .setInputCols(Array("sentence","token"))
+    .setOutputCol("embeddings")
+
+val ner_jsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")
+    .setInputCols(Array("sentence","token","embeddings"))
+    .setOutputCol("ner")
+
+val ner_jsl_converter = new NerConverter()
+    .setInputCols(Array("sentence","token","ner"))
+    .setOutputCol("ner_jsl_chunk")
+    .setWhiteList(Array("External_body_part_or_region", "Internal_organ_or_component"))
+    .setReplaceLabels({"Anatomical_Site": "BodyPart"})
+
+val ner_anatomy = MedicalNerModel.pretrained("ner_anatomy_coarse", "en", "clinical/models")
+    .setInputCols(Array("sentence","token","embeddings"))
+    .setOutputCol("ner_anatomy")
+
+val ner_anatomy_converter = new NerConverterInternal()
+    .setInputCols(Array("sentence", "token", "ner_anatomy"))
+    .setOutputCol("ner_anatomy_chunk")
+    .setReplaceLabels(Map{"Anatomy" -> "BodyPart"})
+
+val ner_oncology_anatomy = MedicalNerModel.pretrained("ner_oncology_anatomy_general", "en", "clinical/models")
+    .setInputCols(Array("sentence","token","embeddings"))
+    .setOutputCol("ner_oncology_anatomy")
+
+val ner_oncology_anatomy_converter = new NerConverter()
+    .setInputCols(Array("sentence","token","ner_oncology_anatomy"))
+    .setOutputCol("ner_oncology_anatomy_chunk")
+    .setWhiteList(Array("Anatomical_Site"))
+    .setReplaceLabels(Map{"Anatomical_Site" -> "BodyPart"})
+
+val chunk_merger = ChunkMergeApproach() \
+    .setInputCols("ner_jsl_chunk", "ner_anatomy_chunk", "ner_oncology_anatomy_chunk")
+    .setOutputCol("ner_chunk")
+
+val chunk2doc = new Chunk2Doc()
+    .setInputCols("ner_chunk")
+    .setOutputCol("ner_chunk_doc")
+
+val sbert_embedder = BertSentenceEmbeddings
+    .pretrained("sbiobert_base_cased_mli","en","clinical/models")
+    .setInputCols(Array("ner_chunk_doc"))
+    .setOutputCol("sbert_embeddings")
+    .setCaseSensitive(False)
+
+val resolver = SentenceEntityResolverModel
+    .pretrained("sbiobertresolve_snomed_bodyStructure", "en", "clinical/models")
+    .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+    .setOutputCol("resolution")
+    .setDistanceFunction("EUCLIDEAN")
+
+val nlpPipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    word_embeddings,
+    ner_jsl,
+    ner_jsl_converter,
+    ner_anatomy,
+    ner_anatomy_converter,
+    ner_oncology_anatomy,
+    ner_oncology_anatomy_converter,
+    chunk_merger,
+    chunk2doc,
+    sbert_embeddings,
+    snomed_resolver
+    ))
+
+val data = Seq("Medical professionals rushed in the bustling emergency room to attend to the patient with alarming symptoms.The attending physician immediately noted signs of respiratory distress, including stridor, a high-pitched sound indicative of upper respiratory tract obstruction.The patient, struggling to breathe, exhibited dyspnea, their chest heaving with each labored breath. Concern heightened when they began experiencing syncope, a sudden loss of consciousness likely stemming from inadequate oxygenation. Further examination revealed a respiratory tract hemorrhage.") .toDF("text")
+
+val model = snomed_pipeline.fit(data)
+
+val result = model.transform(data)
+```
+</div>
+
+## Results
+
+```bash
++-------------------+----------------------------+-----------+--------------------------+--------------------------------------------------+--------------------------------------------------+
+|              chunk|                       label|snomed_code|                resolution|                                         all_codes|                                   all_resolutions|
++-------------------+----------------------------+-----------+--------------------------+--------------------------------------------------+--------------------------------------------------+
+|    coronary artery|                     Anatomy|  181294004|           coronary artery|181294004:::119204004:::360487004:::55537005:::...|coronary artery:::coronary artery part:::segmen...|
+|              renal|                     Anatomy|   64033007|           renal structure|64033007:::243968009:::84924000:::303402001:::3...|renal structure:::renal area:::renal segment:::...|
+|peripheral vascular|                     Anatomy|   51833009|peripheral vascular system|51833009:::840581000:::3058005:::300054001:::28...|peripheral vascular system:::peripheral artery:...|
+|  lower extremities|External_body_part_or_region|   61685007|           lower extremity|61685007:::127951001:::120575009:::182281004:::...|lower extremity:::lower extremity region:::lowe...|
++-------------------+----------------------------+-----------+--------------------------+--------------------------------------------------+--------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|sbiobertresolve_snomed_bodyStructure|
+|Compatibility:|Healthcare NLP 5.3.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[snomed_code]|
+|Language:|en|
+|Size:|197.9 MB|
+|Case sensitive:|false|
+
+## References
+
+This model is trained with the augmented version of NIH September 2023 SNOMED CT United States (US) Edition.