Description This model is a [BioBERT-based] (https://github.com/dmis-lab/biobert) classifier that can classify tweets reporting ADEs (Adverse Drug Events). Predicted Entities ADE, noADE Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutputCol("token") sequence_classifier = MedicalBertForS...| Spark NLP
Description Classify texts/sentences in two categories: True : The sentence is talking about a possible ADE. False : The sentence doesn’t have any information about an ADE. This model is a BioBERT-based classifier. Predicted Entities Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutput...| Spark NLP
Description This model is a BioBERT based classifier that can classify if an article is a randomized clinical trial (RCT) or not. Predicted Entities True, False Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutputCol("token") sequence_classifier = MedicalBertForSequenceClassification.pre...| Spark NLP