Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using mpnet_embeddings_biolord_2023_c embeddings. Additionally, this model returns concept classes of the 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 = MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("biolordresolve_rxnorm_augmented_v2","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.MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("biolordresolve_rxnorm_augmented_v2","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 = MPNetEmbeddings
.pretrained("mpnet_embeddings_biolord_2023_c", "en")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("biolordresolve_rxnorm_augmented_v2", "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 | 860974 | metformin hydrochloride 500 mg [metformin hydrochloride 500 mg] | 860974:::316256:::861007:::860978:::861001:::860995:::861009:::876009:::861017::... | 0.3055:::0.5156:::0.5611:::0.5796:::0.5928:::0.5930:::0.6026:::0.6063:::0.6076::... | 0.0467:::0.1329:::0.1574:::0.1680:::0.1757:::0.1758:::0.1816:::0.1838:::0.1846::... | metformin hydrochloride 500 mg [metformin hydrochloride 500 mg]:::metformin 500 ... | Clinical Drug Comp:::Clinical Drug Comp:::Clinical Drug:::Clinical Drug:::Brande... |
| Lipitor | DRUG | 1177401 | lipitor pill [lipitor pill] | 1177401:::153165:::1177400:::1422091:::617320:::1177403:::1177399:::262095:::617... | 0.4722:::0.5060:::0.5297:::0.6885:::0.7587:::0.7656:::0.7809:::0.7834:::0.7837::... | 0.1115:::0.1280:::0.1403:::0.2370:::0.2878:::0.2931:::0.3049:::0.3069:::0.3071::... | lipitor pill [lipitor pill]:::lipitor [lipitor]:::lipitor oral product [lipitor ... | Branded Dose Group:::Brand Name:::Branded Dose Group:::Branded Dose Group:::Bran... |
| Tylenol PRN | DRUG | 1187313 | tylenol pm oral product [tylenol pm oral product] | 1187313:::1187505:::1187503:::220586:::1187317:::209387:::2634560:::220581:::263... | 0.7145:::0.7300:::0.7331:::0.7363:::0.7419:::0.7500:::0.7551:::0.7552:::0.7583::... | 0.2552:::0.2664:::0.2687:::0.2711:::0.2752:::0.2813:::0.2851:::0.2851:::0.2875::... | tylenol pm oral product [tylenol pm oral product]:::tylenol with codeine oral pr... | Branded Dose Group:::Branded Dose Group:::Branded Dose Group:::Brand Name:::Bran... |
| amoxicillin 500 mg | DRUG | 317616 | amoxicillin 500 mg [amoxicillin 500 mg] | 317616:::308191:::565641:::565639:::565638:::565640:::308192:::575262:::566613::... | 0.4134:::0.4812:::0.4942:::0.4989:::0.5015:::0.5111:::0.5143:::0.5190:::0.5504::... | 0.0855:::0.1158:::0.1221:::0.1245:::0.1258:::0.1306:::0.1323:::0.1347:::0.1514::... | amoxicillin 500 mg [amoxicillin 500 mg]:::amoxicillin 500 mg oral capsule [amoxi... | Clinical Drug Comp:::Clinical Drug:::Branded Drug Comp:::Branded Drug Comp:::Bra... |
Model Information
| Model Name: | biolordresolve_rxnorm_augmented_v2 |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 1.4 GB |
| Case sensitive: | false |