Efficacy of suicide ideation classification based on pain avoidance and the EEG characteristics under self-referential punishment

Bibliographic Data

ID19408376
AuthorsFang Sun (0000-0001-5115-8287, Renmin University of China), Wei Song (0000-0002-5888-3657, Renmin University of China), Xiaotong Wen (0000-0003-0350-6181, Renmin University of China), Huanhuan Li (0009-0006-1538-3480, Renmin University of China), Lisheng OUYANG (Renmin University of China), Shijie Wei (0000-0001-8312-1198, Renmin University of China)
Year2022
Volume54
Issue9
Pages1031
Publication date2022-01-01
Open AccessNo
TypeARTICLE
VenueActa Psychologica Sinica (JOURNAL)
Journal identifiersISSN: 0439-755X
PublisherChina Science Publishing & Media Ltd (PUBLISHER)
DOI10.3724/sp.j.1041.2022.01031
OpenAlexW4286375042
LanguageEN
References cited6

摘要: 采用支持向量机的特征递归选择算法, 创新性采用三维心理痛苦量表和自我参照情感激励延迟任务, 建构自杀意念分类模型的重要特征集, 并比较自杀意念和抑郁的分类模型重要特征集差异。结果发现, 痛苦逃避是自杀意念分类模型的首位特征; 基于痛苦加工特征的自杀意念多模态分类模型效能优良。研究首次证实了在机器学习建构复杂的自杀意念分类模型中, 痛苦逃避及其相关脑电成分的重要性。拓展了结合心理痛苦三因素模型和机器学习算法对自杀预测的临床应用可行性

Cognitive science · Electroencephalography · Ideation · Medical emergency · Poison control · Psychiatry · Psychotherapist · Suicidal ideation · Suicide prevention · Clinical Psychology · Healthcare and Venom Research · Medicine · Psychology

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