Detect Adverse Drug Events (bert-clinical)

Description

Detect adverse drug events in tweets, reviews, and medical text using pretrained NER model.

Predicted Entities

DRUG, ADE

How to use

...
embeddings_clinical = BertEmbeddings.pretrained("biobert_clinical_base_cased")  .setInputCols(["sentence", "token"])  .setInputCols(["sentence", "token"])  .setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_ade_clinicalbert", "en", "clinical/models")   .setInputCols(["sentence", "token", "embeddings"])   .setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["EXAMPLE_TEXT"]]).toDF("text"))

...
val embeddings_clinical = BertEmbeddings.pretrained("biobert_clinical_base_cased")  .setInputCols(["sentence", "token"])
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_ade_clinicalbert", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings"))
  .setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val result = pipeline.fit(Seq.empty[""].toDS.toDF("text")).transform(data)

Model Information

Model Name:	ner_ade_clinicalbert
Compatibility:	Spark NLP for Healthcare 3.0.0+
License:	Licensed
Edition:	Official
Input Labels:	[sentence, token, embeddings]
Output Labels:	[ner]
Language:	en

PREVIOUSDetect Adverse Drug Events

NEXTDetect Adverse Drug Events (healthcare)