%0 Journal Article
%T A Dual-Channel Prediction-Interpretation Framework with Pre-Trained Language Models and SHAP Explainability
%A Hui Nie
%A Xiaoyan Wu
%J Journal of Computer and Communications
%P 116-137
%@ 2327-5227
%D 2025
%I Scientific Research Publishing
%R 10.4236/jcc.2025.133009
%X This study addresses the challenges of data noise and model interpretability in depression diagnosis by proposing an intelligent diagnostic framework based on real-world medical scenarios. Utilizing a labeled dataset of 11,188 Chinese online consultation records, we developed a dual-channel architecture integrating BERT/RoBERTa pre-trained models with the SHAP interpretability framework for depression severity classification. Experimental results demonstrated that the BERT model achieved 92% overall accuracy, with 93% accuracy specifically for severe depression detection. SHAP analysis revealed the model’s focus on clinically relevant features like suicidal tendencies and low mood, showing significant alignment with DSM-5 diagnostic criteria. The study confirms pre-trained models’ capability in extracting pathological semantics from medical texts, while the “prediction-interpretation” framework provides a technical prototype for overcoming clinical application barriers of black-box models.
%K Depression Prediction
%K Explainable Machine Learning
%K BERT Model
%K Patient Narrative
%K SHAP Analysis
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=141586