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“BERT-BASED NATURAL LANGUAGE PROCESSING FOR AUTOMATED CLASSIFICATION OF SUICIDAL IDEATION SEVERITY” Metadata:

  • Title: ➤  BERT-BASED NATURAL LANGUAGE PROCESSING FOR AUTOMATED CLASSIFICATION OF SUICIDAL IDEATION SEVERITY
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  • Internet Archive ID: osf-registrations-aq9hf-v1

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This cross-sectional study investigates the application of BERT-based natural language processing models for automating the classification of suicidal ideation severity from free-text responses to the Columbia Suicide Severity Rating Scale (C-SSRS). Using data from 1,443 adults who had recently attempted suicide across ten Spanish university hospitals (2019-2023), we developed and evaluated machine learning models to classify responses across five key C-SSRS categories: desire to die, active suicidal thoughts, suicidal ideation with methods, interrupted/aborted attempts, and preparatory behaviors. Our BERT-based models achieved high accuracy across all categories (77.3% to 94.44%), with particularly strong performance in identifying active suicidal ideation (F1-score: 0.98) and classifying preparatory acts (F1-score: 0.97). The models outperformed traditional approaches (SVM, Naive Bayes) in global classification tasks (78.3% vs. 74.6% and 71.2% accuracy respectively). We applied SMOTE to address class imbalance and conducted comprehensive error analysis to identify linguistic factors affecting classification performance. The findings demonstrate that transformer-based NLP can effectively analyze structured clinical interview data for suicide risk assessment, potentially enhancing clinical decision-making and enabling more timely interventions. These automated methods may complement clinical expertise in suicide prevention efforts, though human oversight remains essential. The study contributes to the growing application of advanced computational methods in mental health assessment and provides practical insights for clinical practice in suicide risk evaluation.

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  • Added Date: 2025-07-21 07:39:48
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