Predicting the Risk of Hazardous Alcohol Use in Patients with Anxiety Disorders: Development of a Software Prototype Using Vibe Coding

Background: instrumental assessment of hazardous alcohol use risk in patients with anxiety disorders studied insufficiently.

The aim of study was to develop a software prototype for personalized assessment of the risk of hazardous alcohol consumption in patients with anxiety disorders based on machine learning and vibe-coding methods.

Patients and Methods: the work analyzed data from 120 patients with episodic (group 1) and generalized (group 2) anxiety, divided into subgroups by the level of alcohol consumption. Using scales, key psychological markers were identified: the level of neuroticism, preventive and proactive coping.

Results: based on these parameters, a binary logistic regression model with high predictive accuracy (AUC-ROC = 0.991) was built, allowing for a quantitative assessment of the likelihood of risky behavior. The prototype, developed using vibe-coding, implements a graphical interface for automating data collection, risk calculation using the model formula, and visualization of the results. The tool combines clinical interpretability and technical accessibility, allowing doctors and medical psychologists without programming skills to promptly assess risk in real time. The solution facilitates early detection of risk groups, personalization of therapy, and reduction of relapse rates.

Conclusion: the importance of digital technologies in psychiatry and medical psychology is confirmed, but require further validation on representative samples and integration into telemedicine platforms. Future research should focus on improving the interpretability of AI models, ensuring data privacy, and expanding the functionality for monitoring autonomic responses via wearable devices.

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