Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates
<|reference_start|>Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates: An adverse drug effect (ADE) is any harmful event resulting from medical drug treatment. Despite their importance, ADEs are often under-reported in official channels. Some research has therefore turned to detecting discussions of ADEs in social media. Impressive results have been achieved in various attempts to detect ADEs. In a high-stakes domain such as medicine, however, an in-depth evaluation of a model's abilities is crucial. We address the issue of thorough performance evaluation in English-language ADE detection with hand-crafted templates for four capabilities: Temporal order, negation, sentiment, and beneficial effect. We find that models with similar performance on held-out test sets have varying results on these capabilities.<|reference_end|>
@article{macphail2024evaluating,
    title={Evaluating the Robustness of Adverse Drug Event Classification Models
  Using Templates},
    author={Dorothea MacPhail, David Harbecke, Lisa Raithel, Sebastian M"oller},
    journal={arXiv preprint arXiv:2407.02432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02432},
    primaryClass={cs.CL cs.LG}
}