Sentence Entity Resolver for ICD10-PCS (``sbiobert_base_cased_mli`` embeddings)

Description

This model maps extracted medical entities to ICD10-PCS codes using chunk embeddings.

Predicted Entities

ICD10-PCS Codes and their normalized definition with sbiobert_base_cased_mli 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")
 
icd10pcs_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10pcs","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, icd10pcs_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 icd10pcs_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10pcs","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, icd10pcs_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|         resolutions|               codes|
+--------------------+-----+---+---------+-------+----------+--------------------+--------------------+
|        hypertension|   68| 79|  PROBLEM|DWY18ZZ|    0.0626|Hyperthermia of H...|DWY18ZZ:::6A3Z1ZZ...|
|chronic renal ins...|   83|109|  PROBLEM|DTY17ZZ|    0.0722|Contact Radiation...|DTY17ZZ:::04593ZZ...|
|                COPD|  113|116|  PROBLEM|2W04X7Z|    0.0765|Change Intermitte...|2W04X7Z:::0J063ZZ...|
|           gastritis|  120|128|  PROBLEM|04723Z6|    0.0826|Dilation of Gastr...|04723Z6:::04724Z6...|
|                 TIA|  136|138|  PROBLEM|00F5XZZ|    0.1074|Fragmentation in ...|00F5XZZ:::00F53ZZ...|
|a non-ST elevatio...|  182|202|  PROBLEM|B307ZZZ|    0.0750|Plain Radiography...|B307ZZZ:::2W59X3Z...|
|Guaiac positive s...|  208|229|  PROBLEM|3E1G38Z|    0.0886|Irrigation of Upp...|3E1G38Z:::3E1G38X...|
|cardiac catheteri...|  295|317|     TEST|4A0234Z|    0.0783|Measurement of Ca...|4A0234Z:::4A02X4A...|
|                PTCA|  324|327|TREATMENT|03SG3ZZ|    0.0507|Reposition Intrac...|03SG3ZZ:::0GCQ3ZZ...|
|      mid LAD lesion|  332|345|  PROBLEM|02H73DZ|    0.0490|Insertion of Intr...|02H73DZ:::02163Z7...|
+--------------------+-----+---+---------+-------+----------+--------------------+--------------------+

Model Information

Name: sbiobertresolve_icd10pcs
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 ICD10 Procedure Coding System dataset with sbiobert_base_cased_mli sentence embeddings. https://www.icd10data.com/ICD10PCS/Codes