2024-10-07-sbiobertresolve_loinc_augmented_en

docs/_posts/akrztrk/2024-10-07-sbiobertresolve_loinc_augmented_en.md

@@ -0,0 +1,180 @@
+---
+layout: model
+title: Sentence Entity Resolver for LOINC (sbiobert_base_cased_mli embeddings)
+author: John Snow Labs
+name: sbiobertresolve_loinc_augmented
+date: 2024-10-07
+tags: [licensed, en, entity_resolution, loinc, clinical]
+task: Entity Resolution
+language: en
+edition: Healthcare NLP 5.5.0
+spark_version: 3.0
+supported: true
+annotator: SentenceEntityResolverModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model maps extracted clinical NER entities to Logical Observation Identifiers Names and Codes(LOINC) codes using `sbiobert_base_cased_mli` Sentence Bert Embeddings. It trained on the augmented version of the dataset which is used in previous LOINC resolver models. It also provides the official resolution of the codes within the brackets.
+
+## Predicted Entities
+
+`loinc_code`
+
+{:.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_loinc_augmented_en_5.5.0_3.0_1728318394102.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/sbiobertresolve_loinc_augmented_en_5.5.0_3.0_1728318394102.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_model = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") \
+	.setInputCols(["sentence", "token", "embeddings"]) \
+	.setOutputCol("ner")
+
+ner_converter = NerConverterInternal() \
+ 	  .setInputCols(["sentence", "token", "ner"]) \
+	  .setOutputCol("ner_chunk")\
+	  .setWhiteList(["Test"])
+
+chunk2doc = Chunk2Doc()\
+  	.setInputCols("ner_chunk")\
+  	.setOutputCol("ner_chunk_doc")
+
+sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
+	  .setInputCols(["ner_chunk_doc"])\
+	  .setOutputCol("sbert_embeddings")\
+	  .setCaseSensitive(False)
+
+resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc_augmented","en", "clinical/models") \
+	  .setInputCols(["sbert_embeddings"]) \
+	  .setOutputCol("resolution")\
+	  .setDistanceFunction("EUCLIDEAN")
+
+
+nlpPipeline = Pipeline(stages=[document_assembler,
+                               sentence_detector,
+                               tokenizer,
+                               word_embeddings,
+                               ner_model,
+                               ner_converter,
+                               chunk2doc,
+                               sbert_embedder,
+                               resolver])
+
+data = spark.createDataFrame([["""The patient is a 22-year-old female with a history of obesity. She has a Body mass index (BMI) of 33.5 kg/m2, aspartate aminotransferase 64, and alanine aminotransferase 126."""]]).toDF("text")
+
+result = nlpPipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val sentence_detector = 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_model = MedicalNerModel.pretrained("ner_radiology","en","clinical/models")
+  .setInputCols(Array("sentence","token","embeddings"))
+  .setOutputCol("ner")
+
+val ner_converter = new NerConverterInternal()
+  .setInputCols(Array("sentence","token","ner"))
+  .setOutputCol("ner_chunk")
+  .setWhiteList(Array("Test"))
+
+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_loinc_augmented","en","clinical/models")
+  .setInputCols(Array("sbert_embeddings"))
+  .setOutputCol("resolution")
+  .setDistanceFunction("EUCLIDEAN")
+
+val nlpPipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    word_embeddings,
+    ner_model,
+    ner_converter,
+    chunk2doc,
+    sbert_embedder,
+    resolver))
+
+val data = Seq([["""The patient is a 22-year-old female with a history of obesity. She has a Body mass index (BMI) of 33.5 kg/m2, aspartate aminotransferase 64, and alanine aminotransferase 126."""]]).toDF("text")
+
+val result = nlpPipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++--------------------------+-----+---+---------+----------+-------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|                     chunk|begin|end|ner_label|loinc_code|                                            description|                                                 resolutions|                                                   all_codes|                                                  aux_labels|
++--------------------------+-----+---+---------+----------+-------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|                       BMI|   90| 92|     Test|   39156-5|                    BMI [Body mass index (BMI) [Ratio]]|BMI [Body mass index (BMI) [Ratio]]:::BM [IDH1 gene exon ...|39156-5:::100305-2:::LP266933-3:::100225-2:::LP241982-0::...|Observation:::Observation:::Observation:::Observation:::O...|
+|aspartate aminotransferase|  110|135|     Test| LP15426-7|Aspartate aminotransferase [Aspartate aminotransferase]|Aspartate aminotransferase [Aspartate aminotransferase]::...|LP15426-7:::100739-2:::LP307348-5:::LP15333-5:::LP307326-...|Observation:::Observation:::Observation:::Observation:::O...|
+|  alanine aminotransferase|  145|168|     Test| LP15333-5|    Alanine aminotransferase [Alanine aminotransferase]|Alanine aminotransferase [Alanine aminotransferase]:::Ala...|LP15333-5:::LP307326-1:::100738-4:::LP307348-5:::LP15426-...|Observation:::Observation:::Observation:::Observation:::O...|
++--------------------------+-----+---+---------+----------+-------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|sbiobertresolve_loinc_augmented|
+|Compatibility:|Healthcare NLP 5.5.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[loinc_code]|
+|Language:|en|
+|Size:|1.1 GB|
+|Case sensitive:|false|
+
+## References
+This model is trained with augmented version of the LOINC v2.78 dataset released in 2024-08-06.

docs/_posts/akrztrk/2024-10-07-sbiobertresolve_loinc_en.md

@@ -0,0 +1,197 @@
+---
+layout: model
+title: Sentence Entity Resolver for Logical Observation Identifiers Names and Codes (LOINC) codes
+author: John Snow Labs
+name: sbiobertresolve_loinc
+date: 2024-10-07
+tags: [licensed, en, entity_resolution, loinc, clinical]
+task: Entity Resolution
+language: en
+edition: Healthcare NLP 5.5.0
+spark_version: 3.0
+supported: true
+annotator: SentenceEntityResolverModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model maps extracted medical entities to Logical Observation Identifiers Names and Codes (LOINC) codes using `sbiobert_base_cased_mli` Sentence Bert Embeddings.
+It also provides the official resolution of the codes within the brackets.
+
+## Predicted Entities
+
+`loinc_code`
+
+{:.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_loinc_en_5.5.0_3.0_1728321808601.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/sbiobertresolve_loinc_en_5.5.0_3.0_1728321808601.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_model = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models") \
+	.setInputCols(["sentence", "token", "embeddings"]) \
+	.setOutputCol("ner")
+
+ner_converter = NerConverterInternal() \
+ 	  .setInputCols(["sentence", "token", "ner"]) \
+	  .setOutputCol("ner_chunk")\
+	  .setWhiteList(["Test"])
+
+chunk2doc = Chunk2Doc()\
+  	.setInputCols("ner_chunk")\
+  	.setOutputCol("ner_chunk_doc")
+
+sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
+	  .setInputCols(["ner_chunk_doc"])\
+	  .setOutputCol("sbert_embeddings")\
+	  .setCaseSensitive(False)
+
+resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc","en", "clinical/models") \
+	  .setInputCols(["sbert_embeddings"]) \
+	  .setOutputCol("resolution")\
+	  .setDistanceFunction("EUCLIDEAN")
+
+
+nlpPipeline = Pipeline(stages=[document_assembler,
+                               sentence_detector,
+                               tokenizer,
+                               word_embeddings,
+                               ner_model,
+                               ner_converter,
+                               chunk2doc,
+                               sbert_embedder,
+                               resolver])
+
+data = spark.createDataFrame([["""A 65-year-old woman presents to the office with generalized fatigue for the last 4 months.
+  She used to walk 1 mile each evening but now gets tired after 1-2 blocks. She has a history of Crohn disease and hypertension
+  for which she receives appropriate medications. She is married and lives with her husband. She eats a balanced diet that
+  includes chicken, fish, pork, fruits, and vegetables. She rarely drinks alcohol and denies tobacco use. A physical examination
+  is unremarkable. Laboratory studies show the following: Hemoglobin: 9.8g/dL, Hematocrit: 32%, Mean Corpuscular Volume: 110 μm3"""]]).toDF("text")
+
+result = nlpPipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val sentence_detector = 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_model = MedicalNerModel.pretrained("ner_jsl","en","clinical/models")
+  .setInputCols(Array("sentence","token","embeddings"))
+  .setOutputCol("ner")
+
+val ner_converter = new NerConverterInternal()
+  .setInputCols(Array("sentence","token","ner"))
+  .setOutputCol("ner_chunk")
+  .setWhiteList(Array("Test"))
+
+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_loinc","en","clinical/models")
+  .setInputCols(Array("sbert_embeddings"))
+  .setOutputCol("resolution")
+  .setDistanceFunction("EUCLIDEAN")
+
+val nlpPipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    word_embeddings,
+    ner_model,
+    ner_converter,
+    chunk2doc,
+    sbert_embedder,
+    resolver))
+
+val data = Seq([["""A 65-year-old woman presents to the office with generalized fatigue for the last 4 months.
+  She used to walk 1 mile each evening but now gets tired after 1-2 blocks. She has a history of Crohn disease and hypertension
+  for which she receives appropriate medications. She is married and lives with her husband. She eats a balanced diet that
+  includes chicken, fish, pork, fruits, and vegetables. She rarely drinks alcohol and denies tobacco use. A physical examination
+  is unremarkable. Laboratory studies show the following: Hemoglobin: 9.8g/dL, Hematocrit: 32%, Mean Corpuscular Volume: 110 μm3"""]]).toDF("text")
+
+val result = nlpPipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
++-----------------------+-----+---+---------+----------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|                  chunk|begin|end|ner_label|loinc_code|                                                 description|                                                 resolutions|                                                   all_codes|                                                  aux_labels|
++-----------------------+-----+---+---------+----------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+|   physical examination|  490|509|     Test|   29544-4|                       Physical findings [Physical findings]|Physical findings [Physical findings]:::Physical exam by ...|29544-4:::55286-9:::11435-5:::11384-5:::29545-1:::8709-8:...|ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACT...|
+|     Laboratory studies|  528|545|     Test|   26436-6|         Laboratory studies (set) [Laboratory studies (set)]|Laboratory studies (set) [Laboratory studies (set)]:::Lab...|26436-6:::52482-7:::11502-2:::34075-2:::100455-5:::85069-...|ACTIVE:::DISCOURAGED:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:...|
+|             Hemoglobin|  567|576|     Test|   10346-5|Haemoglobin [Hemoglobin A [Units/volume] in Blood by Elec...|Haemoglobin [Hemoglobin A [Units/volume] in Blood by Elec...|10346-5:::15082-1:::11559-2:::2030-5:::34618-9:::38896-7:...|ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACT...|
+|             Hematocrit|  590|599|     Test|   32354-3|Hematocrit [Volume Fraction] of Arterial blood [Hematocri...|Hematocrit [Volume Fraction] of Arterial blood [Hematocri...|32354-3:::20570-8:::11153-4:::13508-7:::104874-3:::42908-...|ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACT...|
+|Mean Corpuscular Volume|  607|629|     Test|   30386-7|Erythrocyte mean corpuscular diameter [Length] [Erythrocy...|Erythrocyte mean corpuscular diameter [Length] [Erythrocy...|30386-7:::101864-7:::20161-6:::18033-1:::19853-1:::101150...|ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACTIVE:::ACT...|
++-----------------------+-----+---+---------+----------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|sbiobertresolve_loinc|
+|Compatibility:|Healthcare NLP 5.5.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[loinc_code]|
+|Language:|en|
+|Size:|666.8 MB|
+|Case sensitive:|false|
+
+## References
+This model is trained with LOINC v2.78 dataset released in 2024-08-06.