Description
The model maps extracted medical entities to their corresponding Snomed codes (Clinical Findings) using sbiobert_base_cased_mli BERT sentence embeddings.
Predicted Entities
Snomed Codes and their normalized definitions
Available as Private API Endpoint
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = 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("embeddings")
ner_jsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner_jsl")
ner_jsl_converter = NerConverterInternal() \
.setInputCols(["sentence", "token", "ner_jsl"]) \
.setOutputCol("ner_jsl_chunk")\
.setWhiteList(["Kidney_Disease", "Cerebrovascular_Disease", "Heart_Disease",
"Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding",
"EKG_Findings", "Communicable_Disease"])\
chunk2doc = Chunk2Doc()\
.setInputCols("ner_jsl_chunk")\
.setOutputCol("ner_chunk_doc")
sbert_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sbert_embeddings")\
.setCaseSensitive(False)
snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings", "en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("snomed_code")\
snomed_pipeline = Pipeline(stages = [
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner_jsl,
ner_jsl_converter,
chunk2doc,
sbert_embeddings,
snomed_resolver
])
text = """The patient exhibited recurrent upper respiratory tract infections, subjective fevers, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia."""
data = spark.createDataFrame([[text]]).toDF("text")
model = snomed_pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained()
.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("embeddings")
val ner_jsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner_jsl")
val ner_jsl_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "clinical_ner"))
.setOutputCol("ner_jsl_chunk")
.setWhiteList(Array("Kidney_Disease", "Cerebrovascular_Disease", "Heart_Disease",
"Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding",
"EKG_Findings", "Communicable_Disease"))
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_jsl_chunk")
.setOutputCol("ner_chunk_doc")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")
.setInputCols("ner_chunk_doc")
.setOutputCol("sbert_embeddings")
val snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("snomed_code")
val new nlpPipeine().setStages(Array(documentAssembler,
sentenceDetector,
tokenizer,
word_embeddings,
ner_jsl_clinical,
ner_jsl_converter,
chunk2doc,
sbert_embedder,
snomed_resolver))
val text= """The patient exhibited recurrent upper respiratory tract infections, subjective fevers, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia."""
val df = Seq(text).toDF(text)
val result= nlpPipeline.fit(df).transform(df)
Results
| | ner_chunk | begin | end | ner_label | snomed_code | description | resolutions |
|--:|-----------------------------------:|------:|----:|--------------------------:|------------:|----------------------------------:|--------------------------------------------------:|
| 0 | upper respiratory tract infections | 32 | 65 | Disease_Syndrome_Disorder | 54150009 | upper respiratory tract infection | upper respiratory tract infection:::upper resp... |
| 1 | fevers | 79 | 84 | VS_Finding | 248425001 | fever | fever:::fever:::intermittent fever:::fever sym... |
| 2 | unintentional weight loss | 87 | 111 | Symptom | 448765001 | unintentional weight loss | unintentional weight loss:::unexplained weight... |
| 3 | night sweats | 129 | 140 | Symptom | 161859009 | night sweats | night sweats:::night sweats:::night sweats:::n... |
| 4 | cachectic | 169 | 177 | Symptom | 238108007 | cachectic | cachectic:::cachexia:::aids with cachexia:::ca... |
| 5 | pale | 183 | 186 | Symptom | 274643008 | pale | pale:::pale color:::pale color:::pale complexi... |
| 6 | hepatosplenomegaly | 202 | 219 | Symptom | 36760000 | hepatosplenomegaly | hepatosplenomegaly:::splenomegaly:::congestive... |
| 7 | pancytopenia | 251 | 262 | Symptom | 127034005 | pancytopenia | pancytopenia:::drug induced pancytopenia:::pan... |
Model Information
| Model Name: | sbiobertresolve_snomed_findings |
| Compatibility: | Healthcare NLP 5.2.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [snomed_code] |
| Language: | en |
| Size: | 715.9 MB |
| Case sensitive: | false |
References
This model is trained with the augmented version of NIH September 2023 SNOMED CT United States (US) Edition.