探索双极性障碍分类的信息项目,使用机器学习与愤怒应对方式,结合情绪障碍问卷和双极光谱诊断量表
Kyungwon Kim1,2,3, Eunsoo Moon1,2,3, Hyunju Lim1,2
1Department of Psychiatry, Pusan National University Hospital, Busan, Republic of Korea.
Psychiatry investigation
|December 12, 2025
概括
机器学习模型有效地区分双相情感障碍与主要抑郁症使用自我报告尺度. 将愤怒应对 (ACS) 与情绪障碍问卷 (MDQ,BSDS) 整合,提高了准确性,特别是当排除特定的行为项目时.
科学领域:
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
- 医疗保健中的机器学习
背景情况:
- 从临床上讲,区分双相情感障碍 (BD) 和主要抑郁障碍 (MDD) 是一个很大的挑战.
- 自我报告问卷是心理健康评估的有价值的工具.
- 机器学习为改善精神病学诊断准确性提供了潜力.
研究的目的:
- 开发和评估一种机器学习分类模型,以区分BD和MDD.
- 评估情绪障碍问卷 (MDQ),双极谱诊断量表 (BSDS) 和愤怒应对量表 (ACS) 的诊断实用性.
主要方法:
- 使用了122名BD和67名MDD患者的数据集.
- 在特征选择中使用递归特征消除.
- 在MDQ,BSDS和ACS项目的组合上训练有素的机器学习分类器.
主要成果:
- 个体尺度的AUC值为0.8212 (MDQ) 和0.7934 (BSDS). 个体尺度的AUC值为0.8212 (MDQ) 和0.7934 (BSDS).
- 结合MDQ和BSDS的AUC为0.8477,在ACS纳入的情况下进一步改善到0.8548.
- 通过排除与冲突,物质使用和侵略性行为相关的特定项目来实现最佳性能.
结论:
- 将愤怒应对方式与情绪症状相结合,可显著提高BD与MDD的诊断准确度.
- 机器学习模型,特别是精细的自我报告尺度,显示出临床诊断的前景.
- 未来的模型应该包含临床和客观数据,以加强分类.
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