Description
This model maps extracted medical entities to ICD10-CM codes using chunk embeddings (augmented with synonyms, four times richer than previous resolver).
Predicted Entities
ICD10-CM Codes and their normalized definition with sbiobert_base_cased_mli sentence embeddings.
Live Demo Open in Colab Copy S3 URI
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")
icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk2doc, sbert_embedder, icd10_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 icd10_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models")
.setInputCols(Array("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, icd10_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| entity| code| all_k_resolutions| all_k_codes|
+--------------------+---------+------+------------------------------------------+---------------------+
| hypertension| PROBLEM| I10|hypertension:::hypertension monitored::...|I10:::Z8679:::I159...|
|chronic renal ins...| PROBLEM| N189|chronic renal insufficiency:::chronic r...|N189:::P2930:::N19...|
| COPD| PROBLEM| J449|copd - chronic obstructive pulmonary di...|J449:::J984:::J628...|
| gastritis| PROBLEM| K2970|gastritis:::chemical gastritis:::gastri...|K2970:::K2960:::K2...|
| TIA| PROBLEM| S0690|cerebral trauma (disorder):::cerebral c...|S0690:::S060X:::G4...|
|a non-ST elevatio...| PROBLEM| I219|silent myocardial infarction (disorder)...|I219:::I248:::I256...|
|Guaiac positive s...| PROBLEM| K921|guaiac-positive stools:::acholic stool ...|K921:::R195:::R15:...|
| mid LAD lesion| PROBLEM| I2102|stemi involving left anterior descendin...|I2102:::I2101:::Q2...|
+--------------------+---------+------+------------------------------------------+---------------------+
Model Information
| Name: | sbiobertresolve_icd10cm_augmented |
| Type: | SentenceEntityResolverModel |
| Compatibility: | Spark NLP 2.6.5 + |
| 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 Clinical Modification dataset with sbiobert_base_cased_mli sentence embeddings.
https://www.icd10data.com/ICD10CM/Codes/