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 sbiobert_base_cased_mli Sentence Bert Embeddings, and has faster load time, with a speedup of about 6X when compared to previous versions. Also the load process now is more memory friendly meaning that the maximum memory required during load time is smaller, reducing the chances of OOM exceptions, and thus relaxing hardware requirements.

Predicted Entities

Predicts Snomed Codes and their normalized definition for each chunk.

Live Demo Open in Colab Download

How to use

sbiobertresolve_snomed_findings resolver model must be used with sbiobert_base_cased_mli as embeddings ner_clinical as NER model. No need to set .setWhiteList().

...
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])

data = 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 = nlpPipeline.fit(data).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 data = Seq("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")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.resolve.snomed.findings").predict("""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 .""")

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

Model Name: sbiobertresolve_snomed_findings
Compatibility: Spark NLP for Healthcare 3.0.4+
License: Licensed
Edition: Official
Input Labels: [ner_chunk, sbert_embeddings]
Output Labels: [snomed_ct_code]
Language: en
Case sensitive: false

Data Source

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