Sentence Entity Resolver for CPT Codes - Augmented (sbiobert_base_cased_mli_onnx)

Description

This model maps clinical procedure and measurement entities to CPT codes using sbiobert_base_cased_mli_onnx embeddings.

Training data: this model is trained on current CPT data, further augmented by John Snow Labs for broader coverage.

CPT resolver models are removed from the Models Hub due to license restrictions and can only be shared with users who already have a valid CPT license. Contact support@johnsnowlabs.com for access.

Live Demo Open in Colab

How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner = MedicalNerModel.pretrained("ner_jsl","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["Procedure","Test","Treatment","Clinical_Dept"])

chunk2doc = Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx","en", "clinical/models")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("sentence_embeddings")\
    .setCaseSensitive(False)

resolver = SentenceEntityResolverModel.load("sbiobertresolve_cpt_augmented")\
    .setInputCols(["sentence_embeddings"])\
    .setOutputCol("cpt_code")\
    .setDistanceFunction("EUCLIDEAN")

pipeline = Pipeline(stages=[
    documentAssembler, sentenceDetector, tokenizer, word_embeddings,
    ner, ner_converter, chunk2doc, embedder, resolver
])

data = spark.createDataFrame([["She was admitted to the hospital with chest pain and found to have bilateral pleural effusion, the right greater than the left. CT scan of the chest also revealed a large mediastinal lymph node. We reviewed the pathology obtained from the pericardectomy in March 2006, which was diagnostic of mesothelioma. At this time, chest tube placement for drainage of the fluid occurred and thoracoscopy, which were performed."]]).toDF("text")
result = pipeline.fit(data).transform(data)

documentAssembler = nlp.DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = nlp.Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner = medical.NerModel.pretrained("ner_jsl","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["Procedure","Test","Treatment","Clinical_Dept"])

chunk2doc = nlp.Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

embedder = nlp.BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx","en", "clinical/models")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("sentence_embeddings")\
    .setCaseSensitive(False)

resolver = medical.SentenceEntityResolverModel.load("sbiobertresolve_cpt_augmented")\
    .setInputCols(["sentence_embeddings"])\
    .setOutputCol("cpt_code")\
    .setDistanceFunction("EUCLIDEAN")

pipeline = nlp.Pipeline(stages=[
    documentAssembler, sentenceDetector, tokenizer, word_embeddings,
    ner, ner_converter, chunk2doc, embedder, resolver
])

data = spark.createDataFrame([["She was admitted to the hospital with chest pain and found to have bilateral pleural effusion, the right greater than the left. CT scan of the chest also revealed a large mediastinal lymph node. We reviewed the pathology obtained from the pericardectomy in March 2006, which was diagnostic of mesothelioma. At this time, chest tube placement for drainage of the fluid occurred and thoracoscopy, which were performed."]]).toDF("text")
result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentenceDetector = SentenceDetectorDLModel
    .pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val word_embeddings = WordEmbeddingsModel
    .pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("word_embeddings")

val ner = MedicalNerModel
    .pretrained("ner_jsl", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "word_embeddings"))
    .setOutputCol("ner")

val nerConverter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")
    .setWhiteList(Array("Procedure", "Test", "Treatment", "Clinical_Dept"))

val chunk2doc = new Chunk2Doc()
    .setInputCols("ner_chunk")
    .setOutputCol("ner_chunk_doc")

val embedder = BertSentenceEmbeddings
    .pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")
    .setInputCols(Array("ner_chunk_doc"))
    .setOutputCol("sentence_embeddings")
    .setCaseSensitive(false)

val resolver = SentenceEntityResolverModel
    .load("sbiobertresolve_cpt_augmented")
    .setInputCols(Array("sentence_embeddings"))
    .setOutputCol("cpt_code")
    .setDistanceFunction("EUCLIDEAN")

val pipeline = new Pipeline().setStages(Array(
    documentAssembler, sentenceDetector, tokenizer, word_embeddings,
    ner, nerConverter, chunk2doc, embedder, resolver
))

val data = Seq("She was admitted to the hospital with chest pain and found to have bilateral pleural effusion, the right greater than the left. CT scan of the chest also revealed a large mediastinal lymph node. We reviewed the pathology obtained from the pericardectomy in March 2006, which was diagnostic of mesothelioma. At this time, chest tube placement for drainage of the fluid occurred and thoracoscopy, which were performed.").toDF("text")
val res = pipeline.fit(data).transform(data)

Results

| ner_chunk            | entity        |   cpt_code | resolution              | all_k_results                                                                       | all_k_cosine_distances                                                              | all_k_resolutions                                                                   |
|:---------------------|:--------------|-----------:|:------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| hospital             | Clinical_Dept |    1021881 | Inpatient Hospital      | 1021881:::94003:::59855:::1013648:::1021883:::1013668:::1021895:::99236:::102187... | 0.0865:::0.1012:::0.1025:::0.1168:::0.1686:::0.1837:::0.1799:::0.1863:::0.1869::... | Inpatient Hospital:::inpatient care:::hospital admission (procedure):::Hospital ... |
| CT scan of the chest | Test          |      71250 | ct scan of chest        | 71250:::70490:::76497:::71260:::74150:::70486:::73200:::70480:::77014:::73700:::... | 0.0080:::0.0772:::0.0809:::0.0957:::0.1037:::0.1028:::0.1056:::0.1088:::0.1178::... | ct scan of chest:::ct scan of neck:::ct scan:::ct scan of chest with contrast:::... |
| pathology            | Clinical_Dept |      88329 | pathology examination   | 88329:::89240:::77768:::1012348:::76098:::0551F:::1011136:::91120:::91299:::1012... | 0.0987:::0.1139:::0.1398:::0.1422:::0.1546:::0.1665:::0.1646:::0.1708:::0.1713::... | pathology examination:::pathology tests:::with lesions:::Anatomic Pathology Proc... |
| pericardectomy       | Procedure     |      33030 | pericardectomy          | 33030:::33020:::64746:::49250:::27350:::68520:::32310:::33025:::32215:::41821:::... | 0.0000:::0.0703:::0.1048:::0.1148:::0.1225:::0.1276:::0.1347:::0.1394:::0.1398::... | pericardectomy:::pericardotomy:::phrenicectomy:::omphalectomy:::patellectomy:::d... |
| chest tube placement | Procedure     |      39503 | insertion of chest tube | 39503:::96440:::32553:::35820:::32100:::36226:::21899:::29200:::0174T:::31502:::... | 0.0331:::0.0847:::0.1056:::0.1311:::0.1333:::0.1391:::0.1364:::0.1382:::0.1414::... | insertion of chest tube:::chest cavity insertion of catheter:::insertion of devi... |
| thoracoscopy         | Procedure     |    1020900 | Thoracoscopy            | 1020900:::32654:::32668:::1006014:::1005962:::35820:::32606:::32555:::31781:::31... | 0.0000:::0.0079:::0.0314:::0.0793:::0.0875:::0.0874:::0.0948:::0.1195:::0.1162::... | Thoracoscopy:::thoracoscopy with:::thoracoscopy (procedure):::thoracoscopy surgi... |

Model Information

Model Name:	sbiobertresolve_cpt_augmented
Compatibility:	Healthcare NLP 6.4.0+
License:	Licensed
Edition:	Official
Input Labels:	[sentence_embeddings]
Output Labels:	[cpt_code]
Language:	en
Size:	364.8 MB
Case sensitive:	false

PREVIOUSMapping Entities with Corresponding CPT Codes

NEXTSentence Entity Resolver for CPT Codes (Procedures and Measurements) - Augmented (sbiobert_base_cased_mli_onnx)