Digital Phenotyping of Psychological Distress in Skin Cancer Patients

Skin cancer patients experience high rates of anxiety and depression, yet routine screening is inconsistent. This study collects social media content from 500 patients over 24 months (12 months retrospective + 12 months prospective). Linguistic markers are identified using a custom Skin Cancer Linguistic Inquiry and Word Count (SC-LIWC) dictionary developed through expert consensus. A hierarchical transformer network with multi-head self-attention is built, comprising word-level (RoBERTa-wwm-ext), post-level (BiGRU+attention), and user-level (8-head self-attention) encoders with platform embeddings to address cross-platform heterogeneity. The model predicts suicide ideation risk (C-SSRS score ≥2) within 12 months, achieving AUC=0.91 in validation. An AI Early Warning System (EWS) is prospectively tested: alerts are triggered at threshold 0.38, reviewed by psychologists (12.4 min/alert), and interventions (crisis hotline, online counselling, psychoeducational materials) are automatically delivered. All data collection complies with China's Personal Information Protection Law (PIPL) and uses a 5-layer de-identification protocol (direct identifier removal, SHA-256 hashing, NER redaction, timestamp shifting, role-based access control). The study also examines cross-platform performance, optimal observation windows, and false-positive patterns to guide refinement