Description
This model maps extracted medical entities to Snomed codes (CT version) using sentence embeddings.
Predicted Entities
Snomed Codes and their normalized definition with sbiobert_base_cased_mli embeddings.
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)
...
val 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")
.setCaseSensitive(False)
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: | Healthcare NLP 3.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk, sbert_embeddings] |
| Output Labels: | [snomed_code] |
| Language: | en |
| Case sensitive: | false |