Description
This model maps drug and substances to UMLS CUI codes. It is trained on ´2024AA´ 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”, “Pharmacologic Substance”, “Antibiotic”, and “Hazardous or Poisonous Substance” categories using ´sbiobert_base_cased_mli´ embeddings.
Predicted Entities
UMLS CUI codes for drug substances
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_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_drug_substance","en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
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")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = 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("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(Array("DRUG"))
val chunk2doc = new 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_drug_substance", "en", "clinical/models")
.setInputCols(Array("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("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 = p_model.fit(data).transform(data)
Results
+-----------------------------+-----+---+------+---------+--------------------------+------------------------------------------------------------+------------------------------------------------------------+
| ner_chunk|begin|end|entity|umls_code| resolved_text| all_k_results| all_k_resolutions|
+-----------------------------+-----+---+------+---------+--------------------------+------------------------------------------------------------+------------------------------------------------------------+
| hydrogen peroxide 30 mg| 26| 48| DRUG| C1126248|hydrogen peroxide 30 mg/ml|C1126248:::C0304655:::C1605252:::C0304656:::C1154260:::C2...|hydrogen peroxide 30 mg/ml:::hydrogen peroxide solution 3...|
| Neosporin Cream| 107|121| DRUG| C0132149| neosporin cream|C0132149:::C0358174:::C0357999:::C0307085:::C0698810:::C0...|neosporin cream:::nystan cream:::nystadermal cream:::nupe...|
|magnesium hydroxide 100mg/1ml| 163|191| DRUG| C1134402|magnesium hydroxide 100 mg|C1134402:::C1126785:::C4317023:::C4051486:::C4047137:::C1...|magnesium hydroxide 100 mg:::magnesium hydroxide 100 mg/m...|
| metformin 1000 mg| 197|213| 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_drug_substance |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [umls_code] |
| Language: | en |
| Size: | 2.9 GB |
| Case sensitive: | false |
References
This model was trained on the ´Clinical Drug´ concept of the ´2024AA´ release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html