Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes according to the National Institute of Health (NIH) database using sbiobert_base_cased_mli Sentence Bert Embeddings.
Predicted Entities
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_model = MedicalNerModel.pretrained("ner_posology_greedy", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverterInternal() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")\
.setWhiteList(['DRUG'])\
.setPreservePosition(False)
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")
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_nih","en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
nlpPipeline = Pipeline(stages=[document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner_model,
ner_converter,
chunk2doc,
sbert_embedder,
rxnorm_resolver])
data = spark.createDataFrame([["""She is given folic acid 1 mg daily , levothyroxine 0.1 mg and aspirin 81 mg daily ."""]]).toDF("text")
results = nlpPipeline.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("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("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("entities")
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_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 rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_nih","en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("resolution")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, ner_model, ner_converter, chunk2doc, sbert_embedder, rxnorm_resolver))
val data = Seq("""She is given folic acid 1 mg daily , levothyroxine 0.1 mg and aspirin 81 mg daily and metformin 100 mg, coumadin 5 mg.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| | sent_id | ner_chunk | entity | rxnorm_code | all_codes | resolutions |
|---:|----------:|:---------------------|:---------|--------------:|:------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 0 | folic acid 1 mg | DRUG | 12281181 | ['12281181', '12283696', '12270292', '12306595', 1227889...| ['folic acid 1 MG [folic acid 1 MG]', 'folic acid 1.1 MG [folic acid 1.1 MG]', 'folic acid 1 MG/ML [folic acid 1 MG/ML]', 'folic a...|
| 1 | 0 | levothyroxine 0.1 mg | DRUG | 12275630 | ['12275630', '12275646', '12301585', '12306484', 1235044...| ['levothyroxine sodium 0.1 MG [levothyroxine sodium 0.1 MG]', 'levothyroxine sodium 0.01 MG [levothyroxine sodium 0.01 MG]', 'levo...|
| 2 | 0 | aspirin 81 mg | DRUG | 12278696 | ['12278696', '12299811', '12298729', '12311168', '1230631...| ['aspirin 81 MG [aspirin 81 MG]', 'aspirin 81 MG [YSP Aspirin] [aspirin 81 MG [YSP Aspirin]]', 'aspirin 81 MG [Med Aspirin] [aspir...|
| 3 | 0 | metformin 100 mg | DRUG | 12282749 | ['12282749', '3735316', '12279966', '1509573', '3736179'... | ['metformin hydrochloride 100 MG/ML [metformin hydrochloride 100 MG/ML]', 'metFORMIN hydrochloride 100 MG/ML [metFORMIN hydrochlor...|
| 4 | 0 | coumadin 5 mg | DRUG | 1768579 | ['1768579', '12534260', '1780903', '1768951', '1510873' ... | ['coumarin 5 MG [coumarin 5 MG]', 'vericiguat 5 MG [vericiguat 5 MG]', 'pridinol 5 MG [pridinol 5 MG]', 'propinox 5 MG [propinox 5...|
Model Information
| Model Name: | sbiobertresolve_rxnorm_nih |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 818.8 MB |
| Case sensitive: | false |
References
Trained on February 2023 with sbiobert_base_cased_mli embeddings.
https://www.nlm.nih.gov/research/umls/rxnorm/docs/rxnormfiles.html