BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
<|reference_start|>BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records: The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making. Despite their success, the potential vulnerabilities of these models remain largely unexplored. This paper delves into the realm of backdoor attacks on clinical language models, introducing an innovative attention-based backdoor attack method, BadCLM (Bad Clinical Language Models). This technique clandestinely embeds a backdoor within the models, causing them to produce incorrect predictions when a pre-defined trigger is present in inputs, while functioning accurately otherwise. We demonstrate the efficacy of BadCLM through an in-hospital mortality prediction task with MIMIC III dataset, showcasing its potential to compromise model integrity. Our findings illuminate a significant security risk in clinical decision support systems and pave the way for future endeavors in fortifying clinical language models against such vulnerabilities.<|reference_end|>
@article{lyu2024badclm:,
    title={BadCLM: Backdoor Attack in Clinical Language Models for Electronic
  Health Records},
    author={Weimin Lyu, Zexin Bi, Fusheng Wang, Chao Chen},
    journal={arXiv preprint arXiv:2407.05213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05213},
    primaryClass={cs.CL cs.AI}
}