Description
This model maps extracted medical entities to ICD10-CM codes using sentence embeddings.
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
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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","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","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, 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|begin|end| entity| code|confidence| all_k_resolutions| all_k_codes|
+--------------------+-----+---+---------+------+----------+--------------------+--------------------+
| hypertension| 68| 79| PROBLEM| I150| 0.2606|Renovascular hype...|I150:::K766:::I10...|
|chronic renal ins...| 83|109| PROBLEM| N186| 0.2059|End stage renal d...|N186:::D631:::P96...|
| COPD| 113|116| PROBLEM| I2781| 0.2132|Cor pulmonale (ch...|I2781:::J449:::J4...|
| gastritis| 120|128| PROBLEM| K5281| 0.1425|Eosinophilic gast...|K5281:::K140:::K9...|
| TIA| 136|138| PROBLEM| G459| 0.1152|Transient cerebra...|G459:::I639:::T79...|
|a non-ST elevatio...| 182|202| PROBLEM| I214| 0.0889|Non-ST elevation ...|I214:::I256:::M62...|
|Guaiac positive s...| 208|229| PROBLEM| K626| 0.0631|Ulcer of anus and...|K626:::K380:::R15...|
|cardiac catheteri...| 295|317| TEST| Z950| 0.2549|Presence of cardi...|Z950:::Z95811:::I...|
| PTCA| 324|327|TREATMENT| Z9861| 0.1268|Coronary angiopla...|Z9861:::Z9862:::I...|
| mid LAD lesion| 332|345| PROBLEM|L02424| 0.1117|Furuncle of left ...|L02424:::Q202:::L...|
+--------------------+-----+---+---------+------+----------+--------------------+--------------------+
Model Information
| Name: | sbiobertresolve_icd10cm |
| 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 Clinical Modification dataset with sbiobert_base_cased_mli sentence embeddings.
https://www.icd10data.com/ICD10CM/Codes/