Artificial intelligence in cognitive psychology — Influence of literature based on artificial intelligence on children's mental disorders

Abstract Mental disorders, such as depression, are increasingly concerned and have significantly affected an individual's physical health. Artificial intelligence (AI) approaches have recently been developed to support mental health professionals, primarily psychiatrists and clinicians, with decision-making based on patients' historical data (e.g., clinical history, behavioural data, social media use, etc.). There is a significant need to cope with fundamental mental health issues in children that can lead to complicated, if not treated at an early stage. Hence, in this paper, Deep Learning assisted Integrated Prediction Model (DLIPM) has been proposed to early forecast and diagnose children's mental illness. In the suggested model, convolutional neural networks (CNN) is first constructed to learn deep-learned patient behavioural data features. By embedding semantic mathematical methods of behaviour or brain dynamic forces into a statistical deep learning framework, insights into disruption, effective classification, and forecast can be achieved. The simulation analysis shows that the proposed model enhances sensitivity rate of 97.9%, specificity rate of 96.7%, recall ratio of 95.6%, the precision ratio of 90.1% of F-measure rate of 95.6%, and less error rate of 9.2% than other existing methods.