Social media platforms have enabled individuals suffering from mental
illnesses to share their lived experiences and find the online support
necessary to cope. However, many users fail to receive genuine clinical
support, thus exacerbating their symptoms. Screening users based on what they
post online can aid providers in administering targeted healthcare and minimize
false positives. Pre-trained Language Models (LMs) can assess users' social
media data and classify them in terms of their mental health risk. We propose a
Question-Answering (QA) approach to assess mental health risk using the
Unified-QA model on two large mental health datasets. To protect user data, we
extend Unified-QA by anonymizing the model training process using differential
privacy. Our results demonstrate the effectiveness of modeling risk assessment
as a QA task, specifically for mental health use cases. Furthermore, the
model's performance decreases by less than 1% with the inclusion of
differential privacy. The proposed system's performance is indicative of a
promising research direction that will lead to the development of privacy-aware
diagnostic systems.

使用 QA 方法和统一 QA 模型在两个大型的精神健康数据集上评估心理健康风险，并通过差分隐私来保护用户数据，结果表明在精神健康用例中将风险评估建模为 QA 任务具有良好的性能，为隐私保护诊断系统的发展提供了一个有前途的研究方向。