Description
This model maps extracted medical entities to HCC codes using Sentence Bert Embeddings.
Predicted Entities
HCC codes and their descriptions.
Live Demo Open in Colab Copy S3 URI
How to use
sbiobertresolve_hcc_augmented resolver model must be used with sbiobert_base_cased_mli as embeddings ner_clinical as NER model. PROBLEM set in .setWhiteList().
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.hcc").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| 139| 0.4357|renal hypertensio...|139:::85:::108:::...|
|chronic renal ins...| 83|109|PROBLEM| 139| 0.9748|chronic renal ins...|139:::140:::136::...|
| COPD| 113|116|PROBLEM| 111| 0.5609|copd - chronic ob...| 111:::112:::84:::85|
| gastritis| 120|128|PROBLEM| 188| 0.1991|functional disord...|188:::6:::75/18::...|
| TIA| 136|138|PROBLEM| 167| 0.3094|cerebral concussi...|167:::100:::167/1...|
|a non-ST elevatio...| 182|202|PROBLEM| 86| 0.4165|silent myocardial...|86:::87:::100:::9...|
|Guaiac positive s...| 208|229|PROBLEM| 188| 0.1492|appendicovesicost...|188:::33:::48:::1...|
| mid LAD lesion| 332|345|PROBLEM| 86| 0.8090|stemi involving l...| 86:::108:::107|
| hypotension| 362|372|PROBLEM| 59| 0.8107|drug-induced hypo...|59:::78:::2:::23:...|
| bradycardia| 378|388|PROBLEM| 96| 0.5205|tachycardia-brady...|96:::59:::78:::23...|
| vagal reaction| 466|479|PROBLEM| 108| 0.4985|vasomotor reactio...|108:::96:::23:::7...|
+--------------------+-----+---+-------+----+----------+--------------------+--------------------+
Model Information
| Model Name: | sbiobertresolve_hcc_augmented |
| Compatibility: | Healthcare NLP 3.0.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [hcc_code] |
| Language: | en |
| Case sensitive: | false |