Description
This pretrained model maps entities with their corresponding RxNorm codes.
Predicted Entities
rxnorm_code
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_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRel("rxnorm_code")
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("sentence", "token", "posology_ner")
.setOutputCol("ner_chunk")
val chunkerMapper = ChunkMapperModel.pretrained("rxnorm_mapper", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
.setRel("rxnorm_code")
val mapper_pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
posology_ner_model,
posology_ner_converter,
chunkerMapper))
val senetence= "The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray"
val data = Seq(senetence).toDF("text")
val result = mapper_pipeline.fit(data).transform(data)
import nlu
nlu.load("en.map_entity.rxnorm_resolver").predict("""The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray""")
Results
+------------------------------+---------------+
|chunk |rxnorm_mappings|
+------------------------------+---------------+
|Zyrtec 10 MG |1011483 |
|Adapin 10 MG Oral Capsule |1000050 |
|Septi-Soothe 0.5 Topical Spray|1000046 |
+------------------------------+---------------+
Model Information
| Model Name: | rxnorm_mapper |
| Compatibility: | Healthcare NLP 3.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [posology_ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 2.3 MB |