Lisheng OUYANG
Biographic Data
| ID | 8917626 |
|---|---|
| NAME | Lisheng OUYANG |
| GIVEN NAMES | Lisheng |
| FAMILY NAME | OUYANG |
| SIGNATURE | OUYANG L |
| AFFILIATIONS | Renmin University of China |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Efficacy of suicide ideation classification based on pain avoidance and the EEG characteristics under self-referential punishment
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
No prominent works on this page.
Efficacy of suicide ideation classification based on pain avoidance and the EEG characteristics under self-referential punishment
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
Clinical Psychology (1 work) · Cognitive science (1 work) · Electroencephalography (1 work) · Healthcare and Venom Research (1 work) · Ideation (1 work) · Medical emergency (1 work) · Medicine (1 work) · Poison control (1 work) · Psychiatry (1 work) · Psychology (1 work)