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Explainable transformer-based framework for suicide risk detection: Deep learning with interpretability for mental health crisis identification
Date Issued
2026
Publisher
MDPI AG
Journal
ISSN
2078-2489
Citation
Information, 2026, vol. 17(5), article no. 448.
Description
Open access
Type
Peer Reviewed Journal Article
Abstract
The public health concern of suicide continues to rise and is increasingly prevalent on social media. The severity of this growing issue highlights the need for improved methods for detecting suicide risk. Many current deep learning approaches do not possess the required level of explainability for application in clinical settings. This study proposes the development of a transformer-based framework called “CrisisFormer,” which was trained on an imbalanced dataset containing 40,000 Reddit posts from the Suicide Watch subreddit and enhanced using DistilBERT. Additionally, the CrisisFormer framework uses three forms of explainable artificial intelligence for interpreting results: SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and transformer attention visualizations. The CrisisFormer framework achieved superior results for detecting the risk of suicide, with 96.25% accuracy, 96.30% precision, 96.25% recall, 96.25% F1 score, and 0.9944 AUC, compared to traditional models such as CNN, LSTM, and BiLSTM. Furthermore, by including clinically relevant suicide terms in its results, CrisisFormer demonstrates a high potential for incorporation into real-world mental health systems for intervention during ongoing mental health crises.
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