Description
This pretrained model maps drugs to their categories and other brands and names. It has two categories called main category and subcategory.
Predicted Entities
main_category, sub_category, other_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")
ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
chunkerMapper = ChunkMapperModel.pretrained("drug_category_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["main_category", "sub_category", "other_name"])
pipeline = Pipeline().setStages([
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
converter,
chunkerMapper])
text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin"
data = spark.createDataFrame([[text]]).toDF("text")
result = 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 ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val chunkerMapper = ChunkMapperModel.pretrained("drug_category_mapper", "en", "clinical/models")
.setInputCols("ner_chunk")
.setOutputCol("mappings")
.setRels(Array("main_category", "sub_category", "other_name"))
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
converter,
chunkerMapper))
val text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin"
val data = Seq(text).toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.map_entity.drug_category").predict("""She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin""")
Results
+-------------+---------------------+-----------------------------------+-----------+
| ner_chunk| main_category| sub_category|other_names|
+-------------+---------------------+-----------------------------------+-----------+
| OxyContin| Pain Management| Opioid Analgesics| Oxaydo|
| folic acid| Nutritionals| Vitamins, Water-Soluble| Folvite|
|levothyroxine|Metabolic & Endocrine| Thyroid Products| Levo T|
| Norvasc| Cardiovascular| Antianginal Agents| Katerzia|
| aspirin| Cardiovascular|Antiplatelet Agents, Cardiovascular| ASA|
| Neurontin| Neurologics| GABA Analogs| Gralise|
+-------------+---------------------+-----------------------------------+-----------+
Model Information
| Model Name: | drug_category_mapper |
| Compatibility: | Healthcare NLP 4.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 526.0 KB |