  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 is publicly available.
