Design of mental health assessment and intervention system based on machine learning

Abstract

Machine learning (ML) techniques have emerged as powerful tools for revolutionizing mental health care delivery through personalized assessment and intervention systems. This abstract summarizes the current state of research in ML-based mental health systems, highlighting their potential, challenges, and future directions. Utilizing a systematic review approach, we synthesized findings from a diverse range of studies focusing on the application of ML algorithms for mental health assessment, prediction, and intervention. Our review revealed promising results in utilizing social media data, smartphone sensor data, electronic health records, and wearable devices to predict and monitor mental health outcomes. Additionally, ML-based interventions, including cognitive-behavioural therapy, mindfulness practices, and personalized recommendations, demonstrated effectiveness in improving mental well-being and reducing symptom severity. However, challenges such as algorithmic bias, data privacy concerns, and the need for interdisciplinary collaboration were also identified. Moving forward, further research is needed to validate findings across diverse populations, optimize algorithm performance, and address ethical considerations to ensure the responsible and equitable integration of ML into mental health care. By leveraging the capabilities of ML, mental health systems have the potential to transform care delivery, making it more accessible, proactive, and personalized for individuals worldwide.