Description
This model maps extracted medical entities to Snomed codes (INT version) using chunk embeddings.
Predicted Entities
Snomed Codes and their normalized definition with sbiobert_base_cased_mli embeddings.
Predicted Entities
Snomed Codes and their normalized definition with sbiobert_base_cased_mli embeddings.
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_int_resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_snomed_findings_int","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_int_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_int_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_snomed_findings_int","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_int_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)
Results
+--------------------+-----+---+---------+---------------+----------+--------------------+--------------------+
| chunk|begin|end| entity| code|confidence| resolutions| codes|
+--------------------+-----+---+---------+---------------+----------+--------------------+--------------------+
| hypertension| 68| 79| PROBLEM| 266285003| 0.8867|rheumatic myocard...|266285003:::15529...|
|chronic renal ins...| 83|109| PROBLEM| 236425005| 0.2470|chronic renal imp...|236425005:::90688...|
| COPD| 113|116| PROBLEM| 413839001| 0.0720|chronic lung dise...|413839001:::41384...|
| gastritis| 120|128| PROBLEM| 266502003| 0.3240|acute peptic ulce...|266502003:::45560...|
| TIA| 136|138| PROBLEM|353101000119105| 0.0727|prostatic intraep...|353101000119105::...|
|a non-ST elevatio...| 182|202| PROBLEM| 233843008| 0.2846|silent myocardial...|233843008:::71942...|
|Guaiac positive s...| 208|229| PROBLEM| 168319009| 0.1167|stool culture pos...|168319009:::70396...|
|cardiac catheteri...| 295|317| TEST| 301095005| 0.2137|cardiac finding::...|301095005:::25090...|
| PTCA| 324|327|TREATMENT|842741000000109| 0.0631|occlusion of post...|842741000000109::...|
| mid LAD lesion| 332|345| PROBLEM| 449567000| 0.0808|overriding left v...|449567000:::25342...|
+--------------------+-----+---+---------+---------------+----------+--------------------+--------------------+
Model Information
| Name: | sbiobertresolve_snomed_findings_int |
| 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 (INT version) Findings with sbiobert_base_cased_mli sentence embeddings.
https://www.snomed.org/