使用数字表型来分类双极性障碍和单极性障碍 - 使用机器学习模型的探索性发现
Maria Faurholt-Jepsen1, Darius Adam Rohani2, Jonas Busk3
1Psychiatric Center Copenhagen, Copenhagen Affective Disorder Research Center (CADIC), Frederiksberg, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
概括
智能手机数据可以帮助区分双相情感障碍 (BD) 和单相情感障碍 (UD),但机器学习模型难以将发现推广到新患者身上. 需要进一步的研究来完善临床使用的数字表型化.
科学领域:
- 数字化表型化是指数字化表型化.
- 机器学习在精神病学中的应用
- 心理健康诊断 心理健康诊断 心理健康诊断
背景情况:
- 双极性障碍 (BD) 和单极性障碍 (UD) 是不同的情绪障碍.
- 区分BD和UD对于有效治疗至关重要.
- 需要客观的数字标记来补充主观的临床评估.
研究的目的:
- 调查BD和UD患者之间的智能手机使用模式的差异.
- 评估机器学习模型的有效性,使用智能手机数据对BD和UD进行分类.
- 评估这些模型的灵敏度,特异性和AUC.
主要方法:
- 收集了6个月的情绪和被动智能手机使用数据的日常自我评估.
- 包括64名BD患者和74名UD患者.
- 采用机器学习模型进行分类,并使用交叉验证分析性能.
主要成果:
- 与UD相比,患有BD的患者在euthymic状态下显示了较少的传入电话.
- 患有BD的患者在抑郁状态下表现出较少的来电和出电话,而非UD.
- 机器学习模型总体实现了高AUC (0.84-0.87),但在离开一个患者的交叉验证 (0.42-0.48) 中显著下降.
结论:
- 智能手机使用数据可以区分BD和UD,特别是在特定的情绪状态.
- 将机器学习模型推广到看不见的个体仍然是一个重大挑战.
- 数字表型显示出作为BD和UD临床评估的辅助工具的潜力.
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