智能化心理健康评估:从神经网络到AI Agent的比较研究OACHSSCD

Mental health issues are a major challenge in global public health.Traditional mental health assessment methods mainly rely on clinical psychologists to conduct interviews,psychological tests,and behavioral observations.Although these methods have certain accuracy in individual assessments,they have long assessment cycles and high costs,making it difficult to meet the needs of large-scale screening.Therefore,there is an urgent need to introduce efficient and intelligent technological means in the field of mental health assessment.The rapid development of artificial intelligence(AI)technology has brought new opportunities for mental health assessment.On the one hand,radial basis function(RBF)neural network simulates the function of brain neurons and uses RBF to process complex mental health data,which has strong adaptability and generalization ability.On the other hand,AI Agent expert system,by integrating expert knowledge and rule-based classification mechanisms,can not only dynamically evaluate and adapt to changes in individual psychological states,but also provide detailed explanations and suggestions,improving the interpretability and credibility of evaluation results.This study introduces RBF neural network and AI Agent expert system into the field of mental health assessment,and compares the advantages and disadvantages of these two representative technologies.The experimental results show that RBF neural network has high accuracy and stability in small and medium-sized data and nonlinear problems,while AI Agent expert system performs well in the interpretability of classification results and knowledge integration,and can achieve efficient mental health assessment at low cost,adapting to different types of assessment tasks.