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 two models: a discriminative BERT-based model called CASE-BERT that
flags potential mental health disorders based on forum text, and a generative
model called CASE-Gemma that extracts key features for a preliminary diagnosis.
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. CASE-Gemma can achieve a BERT
Score of 0.849 on generating diagnoses based on forum text. The effectiveness
of CASE-Gemma is evaluated through both human evaluation and qualitative
methods, with the collaboration of clinical psychologists who provide us with a
set of annotated data for fine-tuning and evaluation. Our code is available at
this https URL

该研究利用自然语言处理（NLP）流程来分析用于咨询的在线心理健康论坛的文本数据，以便快速识别需要紧急心理保健的人群。 通过分析论坛帖子，这些流程可以标记可能需要即时专业关注的用户。 本研究提出了两个模型：一个基于 BERT 的判别模型称为 CASE-BERT，根据论坛文本标记潜在的心理健康障碍；一个生成模型称为 CASE-Gemma，提取初步诊断的关键特征。 CASE-BERT 相较于现有方法表现出更好的性能，对抑郁症和焦虑症两种常见心理健康障碍的 f1 分数分别达到 0.91 和 0.88。CASE-Gemma 可以根据论坛文本生成诊断以达到 BERT 分数为 0.849。对 CASE-Gemma 的有效性进行了人工评估和定性方法评估，并得到了临床心理学家的合作。我们的代码可以在此 https URL 处获得。