Intelligent mental health monitoring in the workplace: A deep learning perspective

Higher work-related stress, anxiety, and burnout have the potential to undermine the well-being of employees and reduce the performance of the organization. The current research paper assumes the provision of an effective AI-based approach, which involves the use of supervised machine learning, sentiment analysis, and natural language processing (NLP) to identify an employee at risk of having mental health issues. The system will pool data on various sources of data such as attendance data, employee feedback, communication trends and, where it would be appropriate, biometric data. The result of such inputs is that the model estimates stress levels with a high degree of accuracy which is about 92.3. Through a secure dashboard, human resources personnel can be able to examine the results that have been processed thus facilitating early identification of stress indicators. When timely measures are taken according to this learning, organizations can maintain a favorable and enabling environment, as well as avoid mental health conditions growing out of control.