A hybrid BERT-BiRNN framework for mental health prediction using textual data

Effective mental health prediction requires training of artificial intelligence algorithms on relevant datasets obtained from individuals suffering from mental illnesses. This study employs a labelled​ text dataset derived from Lyf Support app. To harness the potential of this dataset for the development of a mental health prediction tool, we propose a novel technique that utilises the bidirectional encoder representations from transformers (BERT) model to identify mental health-related text chats. This technique enables effective and accurate identification of textual content relevant to mental health, facilitating the creation of an advanced prediction model. It is capable of extracting word embeddings retaining the semantic and contextual meaning of words. Then, the bidirectional long-short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) models are employed as a sequence processing classifier to effectively analyse and detect signs of mental illness from text chats. Extensive experiments are conducted, and the results are compared against the state-of-the-art models, suggesting that our method outperforms the others by achieving 92.4% accuracy. Overall, this study establishes a good foundation for future research endeavours in mental health prediction approaches. The methodologies and findings presented herein pave the way for further advancements and innovations in this field of study.