Application and progress of machine learning in predicting mental help-seeking behavior
10.3760/cma.j.cn371468-20240613-00275
- VernacularTitle:机器学习在心理求助行为预测中的应用与进展
- Author:
Luo XU
1
;
Yinhuan HU
1
;
Sha LIU
1
;
Xiandong FENG
1
Author Information
1. 华中科技大学同济医学院医药卫生管理学院，武汉 430030
- Publication Type:Journal Article
- Keywords:
Machine learning;
Mental help-seeking;
Mental health;
Behavioral prediction
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2024;33(12):1136-1141
- CountryChina
- Language:Chinese
-
Abstract:
Mental help-seeking is one of the most important way to identify and prevent mental illnesses 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 application 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 development of machine learning methods in the prediction of mental help-seeking behavior, analyses their mechanisms 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 relevant research and clinical work.
