Description
This model maps clinical/veterinary entities to MeSH (Medical Subject Headings) Unique Identifiers (UI) using sbiobert_base_cased_mli_onnx Sentence Embeddings.
Trained on the MeSH 2026 dataset (NLM-native descriptors, entry terms, supplemental concept records, and pharmacologic actions only).
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = 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_model = MedicalNerModel.pretrained("ner_clinical","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
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("sbert_embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_mesh_2026","en","clinical/models")\
.setInputCols(["sbert_embeddings"])\
.setOutputCol("mesh_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages=[\
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
ner_model, ner_converter, chunk2doc, embedder, resolver\
])
data = spark.createDataFrame([["The patient has a long history of diabetes mellitus and hypertension, and presented today with pneumonia and possible myocardial infarction."]]).toDF("text")
result = pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = 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_model = medical.NerModel.pretrained("ner_clinical","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = medical.NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
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("sbert_embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_mesh_2026","en","clinical/models")\
.setInputCols(["sbert_embeddings"])\
.setOutputCol("mesh_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = nlp.Pipeline(stages=[\
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
ner_model, ner_converter, chunk2doc, embedder, resolver\
])
data = spark.createDataFrame([["The patient has a long history of diabetes mellitus and hypertension, and presented today with pneumonia and possible myocardial infarction."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = 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("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("word_embeddings")
val ner_model = MedicalNerModel
.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val chunk2doc = new Chunk2Doc()
.setInputCols(Array("ner_chunk"))
.setOutputCol("ner_chunk_doc")
val embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("sbert_embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_mesh_2026", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("mesh_code")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,
ner_model, ner_converter, chunk2doc, embedder, resolver
))
val data = Seq("The patient has a long history of diabetes mellitus and hypertension, and presented today with pneumonia and possible myocardial infarction.").toDF("text")
val res = pipeline.fit(data).transform(data)
Results
| ner_chunk | entity | MeSH Code | Resolution | all_k_results | all_k_cosine_distances | all_k_resolutions |
|:----------------------|:---------|:------------|:----------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| diabetes mellitus | PROBLEM | D003920 | diabetes mellitus | D003920:::D048909:::D003922:::D003924:::D000099074:::D003923:::D005905:::D003921 | 0.0000:::0.0271:::0.0333:::0.0445:::0.0479:::0.0647:::0.0800:::0.0790 | diabetes mellitus:::complications of diabetes mellitus:::insulin-dependent diabe... |
| hypertension | PROBLEM | D006973 | hypertension | D006973:::D000075222:::D000802:::C072778:::D059468:::D000092244:::D058246:::D009... | 0.0000:::0.0340:::0.0350:::0.0403:::0.0457:::0.0636:::0.0685:::0.0735:::0.0789::... | hypertension:::essential hypertension:::hypertensin:::hypertensive factor:::hype... |
| pneumonia | PROBLEM | D011014 | pneumonia | D011014:::D000092124:::D018410:::D011020:::D000098968:::D011015:::D007711:::D000... | 0.0000:::0.0754:::0.0891:::0.1197:::0.1175:::0.1202:::0.1271:::0.1243:::0.1296::... | pneumonia:::organizing pneumonia:::bacterial pneumonia:::pneumonia, pcp:::commun... |
| myocardial infarction | PROBLEM | D009203 | myocardial infarction | D009203:::D056989:::D056988:::D000072657:::D002544:::D020243 | 0.0000:::0.0269:::0.0306:::0.0354:::0.0547:::0.0583 | myocardial infarction:::inferior myocardial infarction:::anterior wall myocardia... |
Model Information
| Model Name: | sbiobertresolve_mesh_2026 |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sbert_embeddings] |
| Output Labels: | [mesh_code] |
| Language: | en |
| Size: | 1.7 GB |
| Case sensitive: | false |