Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using bge_medembed_large_v0_1 embeddings. Additionally, the model returns concept classes of drugs in the all_k_aux_labels column.
Live Demo Open in Colab Copy S3 URI
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("word_embeddings")
ner_model = MedicalNerModel.pretrained("ner_posology_greedy","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["DRUG"])
chunk2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
embedder = BGEEmbeddings.pretrained("bge_medembed_large_v0_1","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("medembed_large_rxnorm_augmented","en","clinical/models")\
.setInputCols(["embeddings"])\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages=[
documentAssembler, sentenceDetector, tokenizer, word_embeddings,
ner_model, ner_converter, chunk2doc, embedder, resolver
])
data = spark.createDataFrame([["The patient was started on metformin 500 mg twice daily for type 2 diabetes and continued on Lipitor for hyperlipidemia. She takes Tylenol PRN for headaches and was prescribed amoxicillin 500 mg for a sinus infection."]]).toDF("text")
result = pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = 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_posology_greedy","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = medical.NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["DRUG"])
chunk2doc = nlp.Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
embedder = nlp.BGEEmbeddings.pretrained("bge_medembed_large_v0_1","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("medembed_large_rxnorm_augmented","en","clinical/models")\
.setInputCols(["embeddings"])\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = nlp.Pipeline(stages=[
documentAssembler, sentenceDetector, tokenizer, word_embeddings,
ner_model, ner_converter, chunk2doc, embedder, resolver
])
data = spark.createDataFrame([["The patient was started on metformin 500 mg twice daily for type 2 diabetes and continued on Lipitor for hyperlipidemia. She takes Tylenol PRN for headaches and was prescribed amoxicillin 500 mg for a sinus infection."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = 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_posology_greedy", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("DRUG"))
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_chunk")
.setOutputCol("ner_chunk_doc")
val embedder = BGEEmbeddings
.pretrained("bge_medembed_large_v0_1", "en")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("medembed_large_rxnorm_augmented", "en", "clinical/models")
.setInputCols(Array("embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(
documentAssembler, sentenceDetector, tokenizer, word_embeddings,
ner_model, ner_converter, chunk2doc, embedder, resolver
))
val data = Seq("The patient was started on metformin 500 mg twice daily for type 2 diabetes and continued on Lipitor for hyperlipidemia. She takes Tylenol PRN for headaches and was prescribed amoxicillin 500 mg for a sinus infection.").toDF("text")
val res = pipeline.fit(data).transform(data)
Results
| ner_chunk | entity | rxnorm_code | resolution | all_k_results | all_k_distances | all_k_cosine_distances | all_k_resolutions | all_k_aux_labels |
|:-------------------|:---------|--------------:|:----------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| metformin 500 mg | DRUG | 316256 | metformin 500 mg [metformin 500 mg] | 316256:::861007:::860974:::438507:::861025:::332809:::330861:::334886:::316255::... | 0.0000:::0.3281:::0.3371:::0.3512:::0.3548:::0.3948:::0.4065:::0.4222:::0.4393::... | 0.0000:::0.0538:::0.0568:::0.0617:::0.0629:::0.0779:::0.0826:::0.0891:::0.0965::... | metformin 500 mg [metformin 500 mg]:::metformin 500 mg oral tablet [metformin hc... | Clinical Drug Comp:::Clinical Drug:::Clinical Drug Comp:::Clinical Drug Comp:::C... |
| Lipitor | DRUG | 153165 | lipitor [lipitor] | 153165:::1177401:::1177400:::617314:::617318:::617320:::262095:::617317:::617313... | 0.0000:::0.4917:::0.5204:::0.5313:::0.5411:::0.5756:::0.5757:::0.5786:::0.5803::... | 0.0000:::0.1209:::0.1354:::0.1412:::0.1464:::0.1656:::0.1657:::0.1674:::0.1684::... | lipitor [lipitor]:::lipitor pill [lipitor pill]:::lipitor oral product [lipitor ... | Brand Name:::Branded Dose Group:::Branded Dose Group:::Branded Drug:::Branded Dr... |
| Tylenol PRN | DRUG | 202433 | tylenol [tylenol] | 202433:::334048:::1187315:::220581:::161:::330892:::570061:::437174:::336435:::3... | 0.6106:::0.6507:::0.6516:::0.6523:::0.6549:::0.6557:::0.6623:::0.6650:::0.6662::... | 0.1864:::0.2117:::0.2123:::0.2127:::0.2144:::0.2150:::0.2193:::0.2211:::0.2219::... | tylenol [tylenol]:::acetaminophen 350 mg [acetaminophen 350 mg]:::tylenol pill [... | Brand Name:::Clinical Drug Comp:::Branded Dose Group:::Brand Name:::Ingredient::... |
| amoxicillin 500 mg | DRUG | 317616 | amoxicillin 500 mg [amoxicillin 500 mg] | 317616:::575262:::565638:::565639:::791946:::308192:::331059:::565640:::802548::... | 0.0000:::0.2612:::0.2895:::0.2927:::0.2958:::0.2996:::0.3014:::0.3037:::0.3085::... | 0.0000:::0.0341:::0.0419:::0.0428:::0.0438:::0.0449:::0.0454:::0.0461:::0.0476::... | amoxicillin 500 mg [amoxicillin 500 mg]:::amoxicillin 500 mg [amoxicot] [amoxici... | Clinical Drug Comp:::Branded Drug Comp:::Branded Drug Comp:::Branded Drug Comp::... |
Model Information
| Model Name: | medembed_large_rxnorm_augmented |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 1.9 GB |
| Case sensitive: | false |