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.

翻译：社交媒体平台使得患有心理疾病的个体能够分享亲身经历，并找到应对所需的社会支持。然而，许多用户未能获得真正的临床干预，导致症状加重。根据用户在网上的发帖内容进行筛查，可帮助医疗服务提供者实施针对性治疗并减少误报。预训练语言模型（PLMs）能够分析用户的社交媒体数据，并根据心理健康风险对其进行分类。我们提出一种基于问答（QA）的方法，利用Unified-QA模型在两个大型心理健康数据集上评估心理健康风险。为保护用户数据，我们通过差分隐私技术对模型训练过程进行匿名化，扩展了Unified-QA模型。实验结果表明，将风险评估建模为问答任务（尤其针对心理健康场景）具有有效性。此外，引入差分隐私后，模型性能下降幅度低于1%。本系统的性能表明，这一研究方向具有广阔前景，有望推动隐私感知诊断系统的发展。