Description
This model maps medical entities to CPT codes using Sentence Bert Embeddings. The corpus of this model has been extented to measurements, and this model is capable of mapping both procedures and measurement concepts/entities to CPT codes. Measurement codes are helpful in codifying medical entities related to tests and their results.
Predicted Entities
CPT Codes
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = 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"])
c2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en","clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
cpt_resolver = SentenceEntityResolverModel.load("sbiobertresolve_cpt_procedures_measurements_augmented")\
.setInputCols(["sentence_embeddings"]) \
.setOutputCol("cpt_code")\
.setDistanceFunction("EUCLIDEAN")
resolver_pipeline = Pipeline(stages = [
document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter,
c2doc,
sbert_embedder,
cpt_resolver])
model = resolver_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
text='''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, which revealed epithelioid malignant mesothelioma.'''
data = spark.createDataFrame([[text]]).toDF("text")
result = model.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("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 ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("Procedure", "Test"))
val c2doc = new Chunk2Doc()
.setInputCols(Array("ner_chunk"))
.setOutputCol("ner_chunk_doc")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("sentence_embeddings")
.setCaseSensitive(False)
val cpt_resolver = SentenceEntityResolverModel.load("sbiobertresolve_cpt_procedures_measurements_augmented")
.setInputCols(Array("sentence_embeddings"))
.setOutputCol("cpt_code")
.setDistanceFunction("EUCLIDEAN")
val resolver_pipeline = new PipelineModel().setStages(Array(
document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter,
c2doc,
sbert_embedder,
cpt_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, which revealed epithelioid malignant mesothelioma.").toDS.toDF("text")
val results = resolver_pipeline.fit(data).transform(data)
import nlu
nlu.load("en.resolve.cpt.procedures_measurements").predict("""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, which revealed epithelioid malignant mesothelioma.""")
Results
+---------------------+---------+--------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+
| chunk| entity|cpt_code| all_k_resolutions| all_k_codes|
+---------------------+---------+--------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+
| CT scan of the chest| Test| 71250|Diagnostic CT scan of chest [Computed tomography, thorax, diagnostic; without contrast material]:...|71250:::70490:::76497:::71260:::74150:::70486:::73200:::70480:::77014:::73700:::71270:::70491:::7...|
| pericardectomy|Procedure| 33030|Pericardectomy [Pericardiectomy, subtotal or complete; without cardiopulmonary bypass]:::Pericard...|33030:::33020:::64746:::49250:::27350:::68520:::32310:::27340:::33025:::32215:::41821:::1005708::...|
| chest tube placement|Procedure| 39503|Insertion of chest tube [Repair, neonatal diaphragmatic hernia, with or without chest tube insert...|39503:::96440:::32553:::35820:::32100:::36226:::21899:::29200:::0174T:::31502:::31605:::69424:::1...|
|drainage of the fluid|Procedure| 10140|Drainage of blood or fluid accumulation [Incision and drainage of hematoma, seroma or fluid colle...|10140:::40800:::61108:::41006:::62180:::83986:::49082:::27030:::21502:::49323:::32554:::51040:::6...|
| thoracoscopy|Procedure| 1020900|Thoracoscopy [Thoracoscopy]:::Thoracoscopy, surgical; with control of traumatic hemorrhage | [Hea...| 1020900:::32654:::32668:::1006014:::35820:::32606:::32555:::31781:::31515:::29200|
+---------------------+---------+--------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | sbiobertresolve_cpt_procedures_measurements_augmented |
| Compatibility: | Healthcare NLP 3.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [cpt_code] |
| Language: | en |
| Size: | 360.4 MB |
| Case sensitive: | false |
Data Source
Trained on Current Procedural Terminology dataset with sbiobert_base_cased_mli sentence embeddings.
References
CPT resolver models are removed from the Models Hub due to license restrictions and can only be shared with the users who already have a valid CPT license. If you possess one and wish to use this model, kindly contact us at support@johnsnowlabs.com.