Cutting-Edge Mental Health Evaluation and Tracking System
Year: 2025
Type: article
Abstract: Mental health disorders are common, and novel solutions for evaluation and tracking of these disorders are being sought. In this work, we describe a deep learning-based framework that incorporates AI-assisted self-assessment tools and real-time mental health monitoring. Using advanced feature extraction techniques and a wide array of machine learning models, we make predictions about mental health trends to enable timely interventions. Data-driven mental health overview will provide an efficient user experience while balancing the professional perception of the individual. This system encompasses predictive analytics, personalized interventions, and real-time tracking mechanisms that work together to enhance mental health support and implement preventive measures against potential crises. Keywords: Mental Health AI; Deep Learning; Self-Assessment; Predictive Analytics; Feature Extraction; Crisis Prevention
DOI: 10.55041/ijsrem43046
Author: . Syed Abdul Khaliq
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4408870096