Given the current state of the world, because of existing situations around the world, millions of people suffering from mental illnesses feel isolated and unable to receive help in person. Psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. People have increasingly turned to online platforms to express themselves and seek help with their conditions. Deep learning methods have been commonly used to identify and analyze mental health conditions from various sources of information, including social media. Still, they face challenges, including a lack of reliability and overconfidence in predictions resulting in the poor calibration of the models. To solve these issues, We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD by analyzing unstructured user data on Reddit.

翻译：鉴于当前全球局势，由于世界各地现有状况，数百万患有精神疾病的人感到孤立无援，无法获得面对面帮助。心理学研究表明，我们的心理状态可通过语言特征展现于交流之中。人们越来越多地转向在线平台表达自我并寻求疾病援助。深度学习方法已被广泛用于从包括社交媒体在内的多种信息源中识别分析精神健康状况。然而，这些方法仍面临挑战，包括预测可靠性不足及过度自信导致的模型校准不良。为解决这些问题，我们提出UATTA-EB：不确定性感知测试时增强BERT集成方法，通过分析Reddit上的非结构化用户数据，生成可靠且校准良好的预测，用于分类六种可能的精神疾病类型——无病症、抑郁症、焦虑症、双相情感障碍、注意缺陷多动障碍（ADHD）及创伤后应激障碍（PTSD）。