Evaluation demonstrates effective early warning reporting and risk identification in adolescent drug abuse using big data.
Objective: To construct and evaluate a digital monitoring and direct reporting system for youth drug abuse risk, so as to improve the efficiency of risk identification, optimize the intervention mechanism and promote the cross-departmental collaborative management. Method: Based on principles of systems engineering and information security, we developed the Youth Poisoning Network Information Direct Reporting System V1.0. The system integrates six modules: dynamic monitoring, network filtering, psychological support, emergency assistance, early warning reporting, and parent-child communication. It utilizes big data and AI algorithms to conduct real-time screening and early warning of potential poisoning risks among adolescents. Results: The system has implemented multi-dimensional data collection, behavioral trajectory analysis, and intelligent early warning functions. It automatically identifies and reports dangerous keywords, suspicious websites, psychological state changes, and emergency distress behaviors, significantly enhancing the visibility of poisoning risks and response speed. Additionally, modules for parent-child communication, psychological counseling, and cybersecurity education further strengthen collaborative intervention capabilities at both family and school levels. Conclusion: This system provides a digital, process and intelligent solution for adolescent drug abuse risk monitoring, and has significant application value in risk screening, emergency response and psychological support. It has the potential to be an important technical support for adolescent self-harm prevention system.
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Minghui Ye (2025) studied this question.
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