论文标题
对ICD-9代码分配的深度学习系统的对抗性攻击
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment
论文作者
论文摘要
ICD-9代码的手动注释是一个耗时且容易出错的过程。基于深度学习的系统解决了自动化ICD-9编码问题的问题，已经达到了竞争性能。鉴于电子病历的增殖增加，这种自动化系统有望最终取代人类编码人员。在这项工作中，我们研究了一个简单的基于错别字的对抗攻击策略如何影响最先进的模型的性能，以预测排放摘要中最频繁的ICD-9代码。初步结果表明，使用梯度信息的恶意对手可以制作特定的扰动，这些扰动是常规人错别字，在放电摘要中不到3％的单词，以显着影响基线模型的性能。
Manual annotation of ICD-9 codes is a time consuming and error-prone process. Deep learning based systems tackling the problem of automated ICD-9 coding have achieved competitive performance. Given the increased proliferation of electronic medical records, such automated systems are expected to eventually replace human coders. In this work, we investigate how a simple typo-based adversarial attack strategy can impact the performance of state-of-the-art models for the task of predicting the top 50 most frequent ICD-9 codes from discharge summaries. Preliminary results indicate that a malicious adversary, using gradient information, can craft specific perturbations, that appear as regular human typos, for less than 3% of words in the discharge summary to significantly affect the performance of the baseline model.
