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. How to use the model 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:
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