Sentence Entity Resolver for CPT codes (Augmented)

Description

This model maps extracted medical entities to CPT codes using sbiobert_base_cased_mli Sentence Bert Embeddings. This model is enriched with augmented data for better performance.

Predicted Entities

CPT codes and their descriptions.

Live Demo 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")

resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_hcc_augmented","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, 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 sbert_embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli","en","clinical/models")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("sbert_embeddings")

val resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_hcc_augmented","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, icdo_resolver))

val data = 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")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.resolve.cpt.procedures_augmented").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|   all_k_resolutions|         all_k_codes|
+--------------------+-----+---+-------+-----+----------+--------------------+--------------------+
|        hypertension|   68| 79|PROBLEM|36440|    0.3349|Hypertransfusion:...|36440:::24935:::0...|
|chronic renal ins...|   83|109|PROBLEM|50395|    0.0821|Nephrostomy:::Ren...|50395:::50328:::5...|
|                COPD|  113|116|PROBLEM|32960|    0.1575|Lung collapse pro...|32960:::32215:::1...|
|           gastritis|  120|128|PROBLEM|43501|    0.1772|Gastric ulcer sut...|43501:::43631:::4...|
|                 TIA|  136|138|PROBLEM|61460|    0.1432|Intracranial tran...|61460:::64742:::2...|
|a non-ST elevatio...|  182|202|PROBLEM|61624|    0.1151|Percutaneous non-...|61624:::61626:::3...|
|Guaiac positive s...|  208|229|PROBLEM|44005|    0.1115|Enterolysis:::Abd...|44005:::49080:::4...|
|      mid LAD lesion|  332|345|PROBLEM|0281T|    0.2407|Plication of left...|0281T:::93462:::9...|
|         hypotension|  362|372|PROBLEM|99135|    0.9935|Induced hypotensi...|99135:::99185:::9...|
|         bradycardia|  378|388|PROBLEM|99135|    0.3884|Induced hypotensi...|99135:::33305:::3...|
|      vagal reaction|  466|479|PROBLEM|55450|    0.1427|Vasoligation:::Va...|55450:::64408:::7...|
+--------------------+-----+---+-------+-----+----------+--------------------+--------------------+

Model Information

Model Name: sbiobertresolve_cpt_procedures_augmented
Compatibility: Spark NLP for Healthcare 3.0.4+
License: Licensed
Edition: Official
Input Labels: [sentence_embeddings]
Output Labels: [cpt_code_aug]
Language: en
Case sensitive: false