同时利用情绪障碍问卷和双极谱诊断尺度用于基于机器学习的双极障碍和抑郁障碍患者的分类
Kyungwon Kim1,2, Hyun Ju Lim1,3, Je-Min Park1,2
1Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
Psychiatry investigation
|August 1, 2024
概括
机器学习分析的情绪障碍问卷 (MDQ) 和双极谱诊断尺度 (BSDS) 有效地区分双极和抑郁障碍. 结合这些工具可以提高诊断准确度,帮助临床差异化.
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:
- 区分双相情感障碍和抑郁障碍存在重大临床挑战,尽管它们的过程不同.
- 准确的诊断对于有效的治疗和患者管理至关重要.
研究的目的:
- 评估自我报告问卷,情绪障碍问卷 (MDQ) 和双极谱诊断尺度 (BSDS) 在分类双极和抑郁症的有用性.
- 研究基于机器学习的多变量分析的应用,以改善诊断歧视.
主要方法:
- 189名被诊断患有双极性或抑郁症的患者完成了MDQ和BSDS问卷.
- 机器学习分类器,支持向量机 (SVM) 和线性差异分析 (LDA) 用于多变量分析.
- 通过交叉验证和ROC曲线下的面积 (AUC) 评估了分类性能.
主要成果:
- 无论是MDQ还是BSDS,患者组之间都有显著的项目和总得分差异.
- 机器学习分析,特别是SVM,在每个问卷中都取得了很好的差异化 (AUC>0.8).
- 结合MDQ和BSDS数据进一步提高了分类性能,达到0.8762.2的AUC.
结论:
- 应用于MDQ和BSDS数据的机器学习可以显著帮助区分双重情绪和抑郁症.
- 这种方法突出了将高维精神病学数据与机器学习相结合的潜力,以加强精神病障碍的分类.
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