Sentence Entity Resolver for Snomed Concepts, CT version (``sbiobert_base_cased_mli`` embeddings)

Description

This model maps extracted medical entities to Snomed codes (CT version) using chunk embeddings.

Predicted Entities

Snomed Codes and their normalized definition with sbiobert_base_cased_mli embeddings.

Open in Colab Download

How to use

...
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")
 
snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings","en", "clinical/models") \
     .setInputCols(["ner_chunk", "sbert_embeddings"]) \
     .setOutputCol("resolution")\
     .setDistanceFunction("EUCLIDEAN")

nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk2doc, sbert_embedder, snomed_resolver])

model = nlpPipeline.fit(spark.createDataFrame([["This is an 82 - year-old male with a history of prior tobacco use , hypertension , chronic renal insufficiency , COPD , gastritis , and TIA who initially presented to Braintree with a non-ST elevation MI and Guaiac positive stools , transferred to St . Margaret\'s Center for Women & Infants for cardiac catheterization with PTCA to mid LAD lesion complicated by hypotension and bradycardia requiring Atropine , IV fluids and transient dopamine possibly secondary to vagal reaction , subsequently transferred to CCU for close monitoring , hemodynamically stable at the time of admission to the CCU ."]]).toDF("text"))

results = model.transform(data)

...
chunk2doc = 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")
 
val snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings","en", "clinical/models")
     .setInputCols(Array("ner_chunk", "sbert_embeddings"))
     .setOutputCol("resolution")
     .setDistanceFunction("EUCLIDEAN")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk2doc, sbert_embedder, snomed_resolver))

val result = pipeline.fit(Seq.empty["This is an 82 - year-old male with a history of prior tobacco use , hypertension , chronic renal insufficiency , COPD , gastritis , and TIA who initially presented to Braintree with a non-ST elevation MI and Guaiac positive stools , transferred to St . Margaret\'s Center for Women & Infants for cardiac catheterization with PTCA to mid LAD lesion complicated by hypotension and bradycardia requiring Atropine , IV fluids and transient dopamine possibly secondary to vagal reaction , subsequently transferred to CCU for close monitoring , hemodynamically stable at the time of admission to the CCU ."].toDS.toDF("text")).transform(data)

Results

+--------------------+-----+---+---------+---------+----------+--------------------+--------------------+
|               chunk|begin|end|   entity|     code|confidence|         resolutions|               codes|
+--------------------+-----+---+---------+---------+----------+--------------------+--------------------+
|        hypertension|   68| 79|  PROBLEM| 38341003|    0.3234|hypertension:::hy...|38341003:::155295...|
|chronic renal ins...|   83|109|  PROBLEM|723190009|    0.7522|chronic renal ins...|723190009:::70904...|
|                COPD|  113|116|  PROBLEM| 13645005|    0.1226|copd - chronic ob...|13645005:::155565...|
|           gastritis|  120|128|  PROBLEM|235653009|    0.2444|gastritis:::gastr...|235653009:::45560...|
|                 TIA|  136|138|  PROBLEM|275382005|    0.0766|cerebral trauma (...|275382005:::44739...|
|a non-ST elevatio...|  182|202|  PROBLEM|233843008|    0.2224|silent myocardial...|233843008:::19479...|
|Guaiac positive s...|  208|229|  PROBLEM| 59614000|    0.9678|guaiac-positive s...|59614000:::703960...|
|cardiac catheteri...|  295|317|     TEST|301095005|    0.2584|cardiac finding::...|301095005:::25090...|
|                PTCA|  324|327|TREATMENT|373108000|    0.0809|post percutaneous...|373108000:::25103...|
|      mid LAD lesion|  332|345|  PROBLEM|449567000|    0.0900|overriding left v...|449567000:::46140...|
+--------------------+-----+---+---------+---------+----------+--------------------+--------------------+

Model Information

Name: sbiobertresolve_snomed_findings
Type: SentenceEntityResolverModel
Compatibility: Spark NLP 2.6.4 +
License: Licensed
Edition: Official
Input labels: [ner_chunk, chunk_embeddings]
Output labels: [resolution]
Language: en
Dependencies: sbiobert_base_cased_mli

Data Source

Trained on SNOMED (CT version) Findings with sbiobert_base_cased_mli sentence embeddings. http://www.snomed.org/