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Parapar, J., Martín-Rodilla, P., Losada, D.E. and Crestani, F. (2021) Overview of eRisk 2021: Early Risk Prediction on the Internet. In: Candan, K.S., Ionescu, B., Goeuriot, L., et al., Eds., Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2021. Lecture Notes in Computer Science, Springer, 324-344.
https://doi.org/10.1007/978-3-030-85251-1_22

has been cited by the following article:

• TITLE: A Dual-Channel Prediction-Interpretation Framework with Pre-Trained Language Models and SHAP Explainability

  AUTHORS: Hui Nie, Xiaoyan Wu

  KEYWORDS: Depression Prediction, Explainable Machine Learning, BERT Model, Patient Narrative, SHAP Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.3, March 26, 2025

  ABSTRACT: 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.