Clinical Assertion Negation Bert

bvanaken

clinical-assertion-negation-bert

Clinical Assertion / Negation Classification BERT Model description The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? . The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE. The model is based on the ClinicalBERT - Bio + Discharge Summary BERT Model by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge. You can load the model via the transformers library: The model expects input in the form of spans/sentences with one marked entity to classify as PRESENT(0), ABSENT(1) or POSSIBLE(2). The entity in question is identified with the special token [entity] surrounding it. Example input and inference: Cite When working with the model, please cite our paper as follows:
text-classification

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CORe Clinical Outcome Biobert V1$?762

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Creatorbvanaken
Model NameClinical Assertion Negation Bert
Description

Clinical Assertion / Negation Classification BERT ...

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Tagstext-classification
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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