Description
This model maps drug entities and concepts to RxNorm codes using bge_base_en_v1_5_onnx embeddings. It also returns drug concept classes 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_base_en_v1_5_onnx","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("bge_embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("bgeresolve_rxnorm","en","clinical/models")\
.setInputCols(["bge_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_base_en_v1_5_onnx","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("bge_embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("bgeresolve_rxnorm","en","clinical/models")\
.setInputCols(["bge_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_base_en_v1_5_onnx", "en")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("bge_embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("bgeresolve_rxnorm", "en", "clinical/models")
.setInputCols(Array("bge_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:::860974:::332809:::861007:::875864:::316255:::861001:::330861:::860976::... | 0.0000:::0.3536:::0.4056:::0.4113:::0.4221:::0.4441:::0.4536:::0.4557:::0.4646::... | 0.0000:::0.0625:::0.0823:::0.0846:::0.0891:::0.0986:::0.1029:::0.1038:::0.1079::... | metformin 500 mg [metformin 500 mg]:::metformin hydrochloride 500 mg [metformin ... | Clinical Drug Comp:::Clinical Drug Comp:::Clinical Drug Comp:::Clinical Drug:::B... |
| Lipitor | DRUG | 153165 | lipitor [lipitor] | 153165:::1177401:::1177400:::367665:::574676:::617313:::617317:::617319:::617314... | 0.0000:::0.5064:::0.5838:::0.6637:::0.6714:::0.6731:::0.6806:::0.6872:::0.6895::... | 0.0000:::0.1282:::0.1704:::0.2203:::0.2254:::0.2266:::0.2316:::0.2361:::0.2377::... | lipitor [lipitor]:::lipitor pill [lipitor pill]:::lipitor oral product [lipitor ... | Brand Name:::Branded Dose Group:::Branded Dose Group:::Branded Drug Form:::Brand... |
| Tylenol PRN | DRUG | 202433 | tylenol [tylenol] | 202433:::1187315:::220581:::1187314:::1187311:::1187313:::209387:::161:::570070:... | 0.5701:::0.6307:::0.6359:::0.6718:::0.6738:::0.6872:::0.7010:::0.7033:::0.7055::... | 0.1625:::0.1989:::0.2022:::0.2256:::0.2270:::0.2361:::0.2457:::0.2473:::0.2489::... | tylenol [tylenol]:::tylenol pill [tylenol pill]:::tylenol pm [tylenol pm]:::tyle... | Brand Name:::Branded Dose Group:::Brand Name:::Branded Dose Group:::Branded Dose... |
| amoxicillin 500 mg | DRUG | 317616 | amoxicillin 500 mg [amoxicillin 500 mg] | 317616:::565639:::575262:::565638:::565641:::565314:::565640:::563098:::566614::... | 0.0000:::0.1997:::0.2010:::0.2225:::0.2792:::0.2819:::0.2988:::0.3043:::0.3081::... | 0.0000:::0.0199:::0.0202:::0.0248:::0.0390:::0.0397:::0.0446:::0.0463:::0.0475::... | amoxicillin 500 mg [amoxicillin 500 mg]:::amoxicillin 500 mg [amoxil] [amoxicill... | Clinical Drug Comp:::Branded Drug Comp:::Branded Drug Comp:::Branded Drug Comp::... |
Model Information
| Model Name: | bgeresolve_rxnorm |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [bge_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 1.4 GB |
| Case sensitive: | false |