Description
This model maps clinical entities to UMLS CUI codes. It is trained on ´2023AB´ UMLS dataset. The complete dataset has 127 different categories, and this model is trained on the ´Disease or Syndrome´ category using ´sbiobert_base_cased_mli´ embeddings.
Predicted Entities
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("embeddings")
ner_model = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner_jsl")
ner_model_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner_jsl"])\
.setOutputCol("ner_chunk")\
.setWhiteList(['Disease_Syndrome_Disorder','Symptom'])\
chunk2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli",'en','clinical/models')\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sbert_embeddings")\
.setCaseSensitive(false)
resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_disease_syndrome", "en", "clinical/models") \
.setInputCols(["ner_chunk","sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
umls_lp = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
word_embeddings,
ner_model,
ner_model_converter,
chunk2doc,
sbert_embedder,
resolver
])
data = spark.createDataFrame([["""A 35-year-old female with a past medical history significant for rheumatoid arthritis diagnosed 10 years ago, currently managed with methotrexate and prednisone, presented with a three-week history of progressively worsening joint pain and swelling, predominantly involving the wrists, knees, and ankles. She reported morning stiffness lasting over an hour. The patient denied any recent infections to the affected joints."""]]).toDF("text")
result = umls_lp.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.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("embeddings")
val ner_model = MedicalNerModel
.pretrained("ner_jsl", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner_jsl")
val ner_model_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner_jsl"))
.setOutputCol("ner_chunk")
.setWhiteList(["Disease_Syndrome_Disorder","Symptom"])
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_chunk")
.setOutputCol("ner_chunk_doc")
val sbert_embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli", "en","clinical/models")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("sbert_embeddings")
.setCaseSensitive(False)
val resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_umls_disease_syndrome", "en", "clinical/models")
.setInputCols(Array("ner_chunk_doc", "sbert_embeddings"))
.setOutputCol("resolution")
.setDistanceFunction("EUCLIDEAN")
val p_model = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner_model,
ner_model_converter,
chunk2doc,
sbert_embedder,
resolver))
val data = Seq("A 35-year-old female with a past medical history significant for rheumatoid arthritis diagnosed 10 years ago, currently managed with methotrexate and prednisone, presented with a three-week history of progressively worsening joint pain and swelling, predominantly involving the wrists, knees, and ankles. She reported morning stiffness lasting over an hour. The patient denied any recent infections to the affected joints.").toDF("text")
val res = p_model.fit(data).transform(data)
Results
+--------------------+-----+---+-------------------------+---------+--------------------+------------------------------------------------------------+------------------------------------------------------------+
| ner_chunk|begin|end| entity|umls_code| resolved_text| all_k_results| all_k_resolutions|
+--------------------+-----+---+-------------------------+---------+--------------------+------------------------------------------------------------+------------------------------------------------------------+
|rheumatoid arthritis| 65| 84|Disease_Syndrome_Disorder| C0003873|rheumatoid arthritis|C0003873:::C0857204:::C0035436:::C3842272:::C0241786:::C0...|rheumatoid arthritis:::rheumatoid arthropathy:::rheumatic...|
| joint pain| 225|234| Symptom| C0162296| multiple joint pain|C0162296:::C0748680:::C0423690:::C0553642:::C5700083:::C0...|multiple joint pain:::shoulder pain exertional:::facet jo...|
| swelling| 240|247| Symptom| C1411141| wandering swelling|C1411141:::C0037580:::C0281913:::C2938877:::C0497156:::C0...|wandering swelling:::soft tissue swelling:::muscles swell...|
| stiffness| 326|334| Symptom| C1410087| stiffness; spine|C1410087:::C0014481:::C1861404:::C0277460:::C5554232:::C0...|stiffness; spine:::stiff sickness:::thumbs, stiff:::scaly...|
| infections| 388|397|Disease_Syndrome_Disorder| C0851162| infections|C0851162:::C0578491:::C0009450:::C0747002:::C0858744:::C0...|infections:::infections site:::infection:::infections op:...|
| affected joints| 406|420| Symptom| C0022408| joint dysfunction|C0022408:::C0409271:::C5191746:::C0231586:::C4280547:::C0...|joint dysfunction:::derangement of multiple joints:::diso...|
+--------------------+-----+---+-------------------------+---------+--------------------+------------------------------------------------------------+------------------------------------------------------------+
Model Information
| Model Name: | sbiobertresolve_umls_disease_syndrome |
| Compatibility: | Healthcare NLP 5.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [umls_code] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | false |
References
Trained on ´2023AB´ UMLS dataset’s ´Disease or Syndrome´ category. https://www.nlm.nih.gov/research/umls/index.html