The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents CASE-BERT that flags potential mental health disorders based on forum text. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. Our code and data are publicly available.

翻译：心理学家资源的有限性使得高效识别需要紧急心理健康护理的个体变得至关重要。本研究探索利用自然语言处理（NLP）流程分析在线心理健康咨询论坛的文本数据。通过分析论坛帖子，这些流程能够标记可能需要立即获得专业关注的用户。该领域的一个关键挑战在于数据隐私性与稀缺性。为解决此问题，我们提出利用心理健康专业机构易于获取的课程文本对NLP流程进行预训练，以此模拟心理学家的培养过程。本研究提出了CASE-BERT模型，该模型可根据论坛文本标记潜在的心理健康障碍。相较于现有方法，CASE-BERT展现出卓越性能，在抑郁症和焦虑症这两种最常报告的心理健康障碍上分别取得了0.91和0.88的F1分数。我们的代码与数据已公开提供。