Description
This pretrained pipeline maps entities with their corresponding RxNorm codes and normalized RxNorm resolutions.
Predicted Entities
rxnorm_code, normalized_name
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('document')
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols("sentence")\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
posology_ner_model = MedicalNerModel.pretrained("ner_posology_greedy", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("posology_ner")
posology_ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "posology_ner"])\
.setOutputCol("ner_chunk")
chunkerMapper = ChunkMapperModel.pretrained("rxnorm_normalized_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["rxnorm_code", "normalized_name"])
mapper_pipeline = Pipeline().setStages([
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
posology_ner_model,
posology_ner_converter,
chunkerMapper])
data = spark.createDataFrame([["The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray"]]).toDF("text")
result= mapper_pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.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("embeddings")
val posology_ner_model = MedicalNerModel.pretrained("ner_posology_greedy", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("posology_ner")
val posology_ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "posology_ner"))
.setOutputCol("ner_chunk")
val chunkerMapper = ChunkMapperModel.pretrained("rxnorm_normalized_mapper", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
.setRels(Array("rxnorm_code", "normalized_name"))
val mapper_pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
posology_ner_model,
posology_ner_converter,
chunkerMapper))
val data = Seq("The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray").toDS.toDF("text")
val result = mapper_pipeline.fit(data).transform(data)
import nlu
nlu.load("en.map_entity.rxnorm_normalized").predict("""The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray""")
Results
+------------------------------+-----------+--------------------------------------------------------------+
|ner_chunk |rxnorm_code|normalized_name |
+------------------------------+-----------+--------------------------------------------------------------+
|Zyrtec 10 MG |1011483 |cetirizine hydrochloride 10 MG [Zyrtec] |
|Adapin 10 MG Oral Capsule |1000050 |doxepin hydrochloride 10 MG Oral Capsule [Adapin] |
|Septi-Soothe 0.5 Topical Spray|1000046 |chlorhexidine diacetate 0.5 MG/ML Topical Spray [Septi-Soothe]|
+------------------------------+-----------+--------------------------------------------------------------+
Model Information
| Model Name: | rxnorm_normalized_mapper |
| Compatibility: | Healthcare NLP 4.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 10.7 MB |