Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment
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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 significantly affect the performance of the baseline model.
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