机器学习在提高非自杀性自伤预测力中的应用:一项系统综述  

Application of machine learning to improve the predictive performance of non-suicidal self-injury:A systematic review

作　　者：高白雪 谢云龙 罗俊龙 贺雯[1,2] GAO Baixue;XIE Yunlong;LUO Junlong;HE Wen(College of Psychology,Shanghai Normal University,Shanghai 200234,China;Lab for Educational Big Data and Policymaking,Ministry of Education,Shanghai Normal University,Shanghai 200234,China)

机构地区：[1]上海师范大学心理学院 [2]上海师范大学教育部“教育大数据与教育决策”实验室,上海200234

出　　处：《心理科学进展》2025年第3期506-519,共14页Advances in Psychological Science

基　　金：上海市哲学社会科学规划项目(项目编号:2022BSH002)资助。

摘　　要：非自杀性自伤(Non-suicidal Self-injury,NSSI)是重大的公共卫生问题,具有高度污名化、高度复杂性和高度异质性的特点,传统NSSI研究测量和分析方法有限,获得的影响因子预测力较低。近年来机器学习逐步应用于NSSI的分析和建模中,并通过提高NSSI研究工具预测力、增加预测模型复杂度和精确度、区分NSSI类别和亚型,使整体预测性能上升到中等水平。未来需结合NSSI传统理论和研究方法使筛选标准更严格,拓展非问卷NSSI数据与深度学习、无监督学习的结合,根据“先分型、再迁移”的原则增加模型的可复制性与可比性,进一步提高预测性能。Non-suicidal self-injury(NSSI)is a significant public health problem characterised by widespread stigma,high complexity and heterogeneity.Traditional NSSI research measure and analysis methods are limited,resulting in very low predictive power of the identified factors.In recent years,machine learning has gradually been applied to the analysis and modelling of NSSI.Through simplified questionnaire models and complex multimodal data models,the importance of predictive factors can be visualised and more accurate NSSI classification can be achieved,thus improving the overall predictive performance to a moderate level.In the future,it is necessary to combine traditional NSSI theories and methods to make the screening criteria more stringent,and combine unsupervised learning with transfer learning to increase the reproducibility and comparability of the models.Furthermore,combining non-questionnaire NSSI data with deep learning meanwhile is helpful to improve the predictive performance.

关 键 词：机器学习 非自杀性自伤 预测力 应用 

分 类 号：R395[哲学宗教—心理学]