Dual-Channel Prompt-Enhanced BERT-RCNN with Adapters for Schizophrenia Detection from Dialogue Text

Schizophrenia is a severe mental disorder that imposes significant burdens on individuals, families, and society, making early detection critical for improving patient outcomes. However, current diagnosis relies predominantly on subjective clinical assessment. AI-assisted screening presents a promising alternative. In particular, automated analysis of patient language, a direct reflection of thought disorder, offers an objective and highly informative data modality. Despite this potential, research and practice are currently hindered by a scarcity of Chinese textual datasets and an over-reliance on non-linguistic data. To address these limi-tations, we construct the Chinese Schizophrenia Text Dataset (CSTD). Further-more, we propose a novel framework designed to decouple text into "general" and "visual and auditory hallucination" features. This framework utilizes prompt templates to inject clinical prior knowledge, employs BERT for feature extraction, and integrates dual sets of Adapters after each Transformer layer to learn the de-coupled features in parallel. The resulting outputs are fused and processed by a Recurrent Convolutional Neural Network (RCNN) for final classification. Experiments demonstrate that this framework achieves a leading accuracy of 95.58% on the CSTD, outperforming other strong models (including BERT and SBERT-BiLSTM) by at least 7.68%. Crucially, this robust performance highlights the strategic advantage of the parameter-efficient Adapter mechanism, which was deliberately chosen to restrict the number of trainable parameters and effectively prevent overfitting on the limited clinical dataset. These results validate the model's efficacy in recognizing linguistic patterns associated with schizophrenia and support its viability as an auxiliary screening tool in clinical outpatient settings.