在抑郁症,焦灼症,正常悲伤和情感状态中绘制情感概况:通过机器学习方法开发的自我报告选工具
Danil Trofimov1, Maria Zapriy2, Anna Khomenko3
1Department of Digital Data Processing Technologies, MIREA - Russian Technological University, Moscow, Russia.
Psychiatria Danubina
|September 22, 2025
概括
这项研究开发了一种敏感的自我报告查工具,使用机器学习来准确区分心理健康状态,从临床前状态到严重状态,在分类抑郁症,倦怠和悲伤方面达到100%的准确性.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算心理学 计算心理学
- 医疗信息学 医疗信息学
背景情况:
- 现代社会面临着影响心理健康的挑战,增加了情感障碍.
- 需要敏感的工具来区分临床前和临床情绪状态.
- 情绪频谱障碍需要准确且易于使用的查方法.
研究的目的:
- 开发一个敏感的自我问卷,以区分情绪状态.
- 利用机器学习 (ML) 准确分类心理健康状况.
- 确定关键的情感症状领域,以提高诊断准确度.
主要方法:
- 开发了一项两阶段的在线调查,并向大学的工作人员和学生进行管理.
- 机器学习方法用于数据分析和受访者分类.
- 在统计分析和分类任务的基础上,改进了34个问题的最终调查.
主要成果:
- 在优情症,正常悲伤,情绪倦怠和抑郁症组之间发现了显著的差异 (p < .001).
- 基于ML的工具在最终代中实现了对情绪状态的分类100%的准确性.
- 最终的分布显示了乐心症 (38.8%),正常悲伤 (27.3%),情绪疲劳 (25.2%) 和抑郁症 (8.7%).
结论:
- 开发的自我报告工具使用ML准确地分类了情绪频谱.
- 将临床评估与ML算法的整合提高了诊断准确性并降低了成本.
- 需要进一步的临床研究来完善该工具的敏感症状领域.
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