Electronic healthcare records are vital for patient safety as they document
conditions, plans, and procedures in both free text and medical codes. Language
models have significantly enhanced the processing of such records, streamlining
workflows and reducing manual data entry, thereby saving healthcare providers
significant resources. However, the black-box nature of these models often
leaves healthcare professionals hesitant to trust them. State-of-the-art
explainability methods increase model transparency but rely on human-annotated
evidence spans, which are costly. In this study, we propose an approach to
produce plausible and faithful explanations without needing such annotations.
We demonstrate on the automated medical coding task that adversarial robustness
training improves explanation plausibility and introduce AttInGrad, a new
explanation method superior to previous ones. By combining both contributions
in a fully unsupervised setup, we produce explanations of comparable quality,
or better, to that of a supervised approach. We release our code and model
weights.

在这项研究中，我们提出了一种无需人工注释的方法来生成可信和忠实的解释。我们展示了对自动医学编码任务的对抗鲁棒性训练如何提高解释的可信度，并引入了一种比现有方法更好的新解释方法 AttInGrad。通过在完全无监督的设置中结合这两个贡献，我们生成了与有监督方法相当甚至更好的解释。我们发布了我们的代码和模型权重。