Description
This model maps drug entities to UMLS CUI codes. It is trained on 2023AB release of the Unified Medical Language System (UMLS) dataset. The complete dataset has 127 different categories, and this model is trained on the Clinical Drug category using sbiobert_base_cased_mli embeddings.
Predicted Entities
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('document')
sentence_detector = SentenceDetector()\
.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_posology_greedy", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("posology_ner")
ner_model_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "posology_ner"])\
.setOutputCol("posology_ner_chunk")\
.setWhiteList(["DRUG"])
chunk2doc = Chunk2Doc().setInputCols("posology_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_clinical_drugs","en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages = [document_assembler, sentence_detector, tokenizer, word_embeddings, ner_model, ner_model_converter, chunk2doc, sbert_embedder, resolver])
data = spark.createDataFrame([["""She was immediately given hydrogen peroxide 30 mg to treat the infection on her leg, and has been advised Neosporin Cream for 5 days. She has a history of taking magnesium hydroxide 100mg/1ml and metformin 1000 mg."""]]).toDF("text")
results = pipeline.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_posology_greedy", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("posology_ner")
val ner_model_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "posology_ner"))
.setOutputCol("posology_ner_chunk")
.setWhiteList("DRUG")
val chunk2doc = Chunk2Doc().setInputCols("posology_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_clinical_drugs", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("resolution")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, ner_model, ner_model_converter, chunk2doc, sbert_embedder, resolver))
val data = Seq("She was immediately given hydrogen peroxide 30 mg to treat the infection on her leg, and has been advised Neosporin Cream for 5 days. She has a history of taking magnesium hydroxide 100mg/1ml and metformin 1000 mg.").toDF("text")
val res = pipeline.fit(data).transform(data)
Results
+-----------------------------+------+---------+--------------------------+------------------------------------------------------------+------------------------------------------------------------+
| ner_chunk|entity|umls_code| description| all_k_results| all_k_resolutions|
+-----------------------------+------+---------+--------------------------+------------------------------------------------------------+------------------------------------------------------------+
| hydrogen peroxide 30 mg| DRUG| C1126248|hydrogen peroxide 30 mg/ml|C1126248:::C0304655:::C1605252:::C0304656:::C1154260:::C2...|hydrogen peroxide 30 mg/ml:::hydrogen peroxide solution 3...|
| Neosporin Cream| DRUG| C0132149| neosporin cream|C0132149:::C4722788:::C0704071:::C0698988:::C1252084:::C3...|neosporin cream:::neomycin sulfate cream:::neosporin topi...|
|magnesium hydroxide 100mg/1ml| DRUG| C1134402|magnesium hydroxide 100 mg|C1134402:::C1126785:::C4317023:::C4051486:::C4047137:::C1...|magnesium hydroxide 100 mg:::magnesium hydroxide 100 mg/m...|
| metformin 1000 mg| DRUG| C0987664| metformin 1000 mg|C0987664:::C2719784:::C0978482:::C2719786:::C4282269:::C2...|metformin 1000 mg:::metformin hydrochloride 1000 mg:::met...|
+-----------------------------+------+---------+--------------------------+------------------------------------------------------------+------------------------------------------------------------+
Model Information
| Model Name: | sbiobertresolve_umls_clinical_drugs |
| Compatibility: | Healthcare NLP 5.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [umls_code] |
| Language: | en |
| Size: | 1.9 GB |
| Case sensitive: | false |
References
Trained on the Clinical Drug concepts of the 2023AB release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html