BadCLM：面向电子健康记录的临床语言模型后门攻击
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
AI总结：
针对电子健康记录临床语言模型的安全隐患，本文提出基于注意力的后门攻击方法 BadCLM，并在 MIMIC III 的院内死亡率预测任务中验证其隐蔽性与破坏性。
摘要：
集成到电子健康记录（EHR）中、用于临床决策支持的临床语言模型的出现，标志着一项重大进展；这些模型利用临床笔记的深度来改进决策。尽管它们取得了成功，但其潜在脆弱性在很大程度上仍未得到探索。本文深入研究针对临床语言模型的后门攻击领域，提出一种创新的、基于注意力的后门攻击方法 BadCLM（Bad Clinical Language Models）。该技术秘密地在模型中植入后门，使其在输入中存在预定义触发器时产生错误预测，而在其他情况下仍能准确运行。我们通过使用 MIMIC III 数据集开展院内死亡率预测任务，证明了 BadCLM 的有效性，展示了其破坏模型完整性的潜力。我们的研究结果揭示了临床决策支持系统中的重大安全风险，并为未来加强临床语言模型以抵御此类脆弱性的工作铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
