Description
This model maps medical entities to SNOMED codes using sent_biobert_clinical_base_cased Sentence Bert Embeddings. The corpus of this model includes Procedures and Measurement domains.
Predicted Entities
SNOMED codes from Procedures and Measurements
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings\
.pretrained("sent_biobert_clinical_base_cased", "en")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")
resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_clinical_snomed_procedures_measurements", "en", "clinical/models") \
.setInputCols(["ner_chunk", "sbert_embeddings"]) \
.setOutputCol("cpt_code")
pipelineModel = PipelineModel(
stages = [
documentAssembler,
sbert_embedder,
resolver])
l_model = LightPipeline(pipelineModel)
result = l_model.fullAnnotate(['coronary calcium score', 'heart surgery', 'ct scan', 'bp value'])
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings
.pretrained("sent_biobert_clinical_base_cased", "en")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sbert_embeddings")
val resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_clinical_snomed_procedures_measurements", "en", "clinical/models)
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("cpt_code")
val pipelineModel= new PipelineModel().setStages(Array(document_assembler, sbert_embedder, resolver))
val l_model = LightPipeline(pipelineModel)
val result = l_model.fullAnnotate(Array("coronary calcium score", "heart surgery", "ct scan", "bp value"))
import nlu
nlu.load("en.resolve.clinical_snomed_procedures_measurements").predict("""coronary calcium score""")
Results
| | chunk | code | code_description | all_k_code_desc | all_k_codes |
|---:|:-----------------------|----------:|:------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | coronary calcium score | 450360000 | Coronary artery calcium score | ['450360000', '450734004', '1086491000000104', '1086481000000101', '762241007'] | ['Coronary artery calcium score', 'Coronary artery calcium score', 'Dundee Coronary Risk Disk score', 'Dundee Coronary Risk rank', 'Dundee Coronary Risk Disk'] |
| 1 | heart surgery | 2598006 | Open heart surgery | ['2598006', '64915003', '119766003', '34068001', '233004008'] | ['Open heart surgery', 'Operation on heart', 'Heart reconstruction', 'Heart valve replacement', 'Coronary sinus operation'] |
| 2 | ct scan | 303653007 | CT of head | ['303653007', '431864000', '363023007', '418272005', '241577003'] | ['CT of head', 'CT guided injection', 'CT of site', 'CT angiography', 'CT of spine'] |
| 3 | bp value | 75367002 | Blood pressure | ['75367002', '6797001', '723232008', '46973005', '427732000'] | ['Blood pressure', 'Mean blood pressure', 'Average blood pressure', 'Blood pressure taking', 'Speed of blood pressure response'] |
Model Information
| Model Name: | sbiobertresolve_clinical_snomed_procedures_measurements |
| Compatibility: | Healthcare NLP 3.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_chunk_embeddings] |
| Output Labels: | [output] |
| Language: | en |
| Case sensitive: | false |
Data Source
Trained on SNOMED code dataset with sent_biobert_clinical_base_cased sentence embeddings.