Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using sbiobert_base_cased_mli Sentence Bert Embeddings. Additionally, this model returns concept classes of the drugs in all_k_aux_labels column in the metadata.
Predicted Entities
RxNorm Codes, Concept Classes
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = 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 = 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"])
c2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")\ \
.setInputCols(["sentence_embeddings"]) \
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
resolver_pipeline = Pipeline(stages = [document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter,
c2doc,
sbert_embedder,
rxnorm_resolver])
text= "The patient was prescribed aspirin and and Albuterol inhaler, two puffs every 4 hours as needed for asthma. He was seen by the endocrinology service and she was discharged on avandia 4 mg at night , Coumadin 5 mg with meals , and metformin 1000 mg two times a day and Lisinopril 10 mg daily"
data = spark.createDataFrame([[text]]).toDF("text")
result = resolver_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("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 = 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("ner_chunk")
.setWhiteList(Array("DRUG"))
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")\
.setCaseSensitive(False)
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")
.setInputCols("sbert_embeddings")
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter,
chunk2doc,
sbert_embedder,
rxnorm_resolver))
val data = Seq("The patient was prescribed aspirin and and Albuterol inhaler, two puffs every 4 hours as needed for asthma. He was seen by the endocrinology service and she was discharged on Coumadin 5 mg with meals , and metformin 1000 mg two times a day and Lisinopril 10 mg daily").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| | ner_chunk | entity | RxNormCode | resolution | all_k_results | all_k_distances | all_k_cosine_distances | all_k_resolutions | all_k_aux_labels |
|---:|-------------------|:---------|------------|-----------------------------------------------------------------|--------------------------------------------------|-----------------------------------------------|-----------------------------------------------|--------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| 0 | aspirin | DRUG | 1537020 | aspirin Effervescent Oral Tablet | 1537020:::1191:::1295740:::405403:::218266... | 0.0000:::0.0000:::4.1826:::5.7007:::6.0877... | 0.0000:::0.0000:::0.0292:::0.0550:::0.0624... |aspirin Effervescent Oral Tablet:::aspirin [aspirin]:::aspirin Oral Powder Product... | Clinical Drug Form:::Ingredient:::Clinical Dose Group:::Brand Name:::Brand Na...|
| 1 | Albuterol inhaler | DRUG | 745678 | albuterol metered dose inhaler [albuterol metered dose inhaler] | 745678:::2108226:::1154602:::2108233:::2108228...| 4.9847:::5.1028:::5.4746:::5.7809:::6.28597...| 0.0414:::0.0439:::0.0505:::0.0562:::0.0676 ...|albuterol metered dose inhaler [albuterol metered dose inhaler]:::albuterol inhalation solution...| Clinical Drug Form:::Clinical Drug Form:::Clinical Dose Group:::Clinical Drug...|
| 2 | Coumadin 5 mg | DRUG | 855333 | warfarin sodium 5 MG [Coumadin] | 855333:::432467:::438740:::153692:::352120... | 0.0000:::4.0885:::4.0885:::5.3065:::5.51327...| 0.0000:::0.0287:::0.0287:::0.0479:::0.0518... |warfarin sodium 5 MG [Coumadin]:::coumarin 5 MG Oral Tablet:::coumarin 5 mg [coumarin 5 mg]:::o...| Branded Drug Comp:::Clinical Drug:::Clinical Drug Comp:::Branded Drug:::Brand...|
| 3 | metformin 1000 mg | DRUG | 316255 | metformin 1000 mg [metformin 1000 mg] | 316255:::860995:::860999:::860997:::861014. | 0.0000:::5.2988:::5.2988:::5.9071:::6.3066... | 0.0000:::0.0445:::0.0445:::0.0553:::0.0632... |metformin 1000 mg [metformin 1000 mg]:::metformin hydrochloride 1000 mg [metformin hydrochlore... | Clinical Drug Comp:::Clinical Drug Comp:::Clinical Drug:::Branded Drug Comp::...|
| 4 | Lisinopril 10 mg | DRUG | 314076 | lisinopril 10 MG Oral Tablet | 314076:::316151:::567576:::565846:::389184... | 0.0000:::0.0000:::3.6543:::4.2782:::4.2805... | 0.0000:::0.0000:::0.0234:::0.0325:::0.0315... |lisinopril 10 MG Oral Tablet:::lisinopril 10 mg [lisinopril 10 mg]:::lisinopril 10 mg [prinivil...| Clinical Drug:::Clinical Drug Comp:::Branded Drug Comp:::Branded Drug Comp:::...|
Model Information
| Model Name: | sbiobertresolve_rxnorm_augmented |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 1.1 GB |
| Case sensitive: | false |
References
In this model, RxNorm version 5.0 was used for the training.