Mental health has always been neglected by many healthcare systems even though it is an important part of human health. As the epidemic of mental health disorders and the lack of mental health professionals increase, this issue needs to be addressed using new, evidence-based practices. The given study proposes an AI-based tool to control the mental health, evaluate it, and provide assistance based on machine learning algorithms and natural language processing (NLP) solutions. To validate and train its predictive models, the system combines information of publicly available datasets, such as DAIC-WOZ and MIMIC-III. DAIC-WOZ dataset offers annotated speech and facial expression information to assess the presence of depression and MIMIC-III provides physiological signs associated with stress and anxiety states of individuals. The suggested system will allow integrating mobile apps and wearable sensors in one system that will process real-time data, including, but not limited to, speech signs, voice tones, and behavioral responses. Such information is analyzed in AI models to generate individual evaluations of mental health and early warnings. It is our hope that by introducing this system we can alleviate some of the burden on mental health professionals and make people more independent in taking care of their psychological needs. The major performance indicators reveal the system potential: the sentiment analysis module scored 88%; the speech emotion recognition model scored 85%; the physiological signal analysis scored 82%. The response time of the system is less than 3 seconds, which means that it is possible to respond instantly. Moreover, the AI-models provide reminders to therapy, suggestions on peer support, and personal advice. This paper assesses the system development process, structure and ethical aspects, and possibilities of implementing it in reality.
Hussain et al. (Mon,) studied this question.