Application and progress of machine learning in predicting mental help-seeking behavior
Mental help-seeking is one of the most important way to identify and prevent mental ill-nesses and disorders,but many people in need often lack the willingness to actively seek help.So accurately predicting mental help-seeking tendencies and behaviors is of great importance for the early prevention and intervention of mental health problems.With the development of artificial intelligence technology,the appli-cation of machine learning to the prediction of mental help-seeking behavior has become more widespread and has demonstrated better predictive efficacy than traditional methods.By collecting and analyzing multiple types of data,machine learning models can more comprehensively identify potential predictors of help-seeking behavior and provide a scientific basis for personalized intervention.Given the lack of systematic integration of previous research in this area,this paper provides a comprehensive review of the application and develop-ment of machine learning methods in the prediction of mental help-seeking behavior,analyses their mecha-nisms and advantages,discusses the current status of their application in different populations,and proposes directions for future research and perspectives,with the aim of providing useful references and insights for rel-evant research and clinical work.
NETL
NSTL
万方数据
