Sentence Entity Resolver for CPT (``sbiobert_base_cased_mli`` embeddings)

Description

This model maps extracted medical entities to CPT codes using chunk embeddings.

Predicted Entities

CPT Codes and their normalized definition with sbiobert_base_cased_mli sentence 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")
 
cpt_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_cpt","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, cpt_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 cpt_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_cpt","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, cpt_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|   all_k_resolutions|         all_k_codes|
+--------------------+-----+---+---------+-----+----------+--------------------+--------------------+
|        hypertension|   68| 79|  PROBLEM|49425|    0.0967|Insertion of peri...|49425:::36818:::3...|
|chronic renal ins...|   83|109|  PROBLEM|50070|    0.2569|Nephrolithotomy; ...|50070:::49425:::5...|
|                COPD|  113|116|  PROBLEM|49425|    0.0779|Insertion of peri...|49425:::31592:::4...|
|           gastritis|  120|128|  PROBLEM|43810|    0.5289|Gastroduodenostom...|43810:::43880:::4...|
|                 TIA|  136|138|  PROBLEM|25927|    0.2060|Transmetacarpal a...|25927:::25931:::6...|
|a non-ST elevatio...|  182|202|  PROBLEM|33300|    0.3046|Repair of cardiac...|33300:::33813:::3...|
|Guaiac positive s...|  208|229|  PROBLEM|47765|    0.0974|Anastomosis, of i...|47765:::49425:::1...|
|cardiac catheteri...|  295|317|     TEST|62225|    0.1996|Replacement or ir...|62225:::33722:::4...|
|                PTCA|  324|327|TREATMENT|60500|    0.1481|Parathyroidectomy...|60500:::43800:::2...|
|      mid LAD lesion|  332|345|  PROBLEM|33722|    0.3097|Closure of aortic...|33722:::33732:::3...|
+--------------------+-----+---+---------+-----+----------+--------------------+--------------------+

Model Information

Name: sbiobertresolve_cpt
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 Current Procedural Terminology dataset with sbiobert_base_cased_mli sentence embeddings.