使用数字表型和隐藏的马尔科夫模型揭示健康和临床人群中的社会状态:观察性研究
Imogen E Leaning1,2, Andrea Costanzo3, Raj Jagesar3
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
Journal of medical Internet research
|April 28, 2025
概括
隐藏的马尔科夫模型 (HMMs) 提供了一种分析智能手机数据以进行数字表型化的实用方法,揭示了阿尔茨海默病和记忆障碍中的社会活动减少.
科学领域:
- 数字化表型化是指数字化表型化.
- 计算精神病学是一种计算精神病学.
- 行为数据分析行为数据分析.
背景情况:
- 大脑疾病表现出诸如社会退缩之类的行为症状.
- 智能手机提供客观的,实时的行为数据收集,避免回忆偏差.
- 分析缺乏时间特征的复杂智能手机数据带来了挑战.
研究的目的:
- 调查隐藏的马尔科夫模型 (HMMs) 用于建模智能手机时间序列数据.
- 评估HMM作为社会功能数字表型的方法.
- 探索HMMs在临床人群中识别行为差异方面的实用性.
主要方法:
- 将隐藏的马尔科夫模型 (HMM) 应用于来自健康对照组 (HCs) 和患有精神分裂症,阿尔茨海默氏症 (AD) 和记忆障碍的参与者的智能手机使用数据.
- 在HC子集上训练HMM以识别社会活跃和不活跃的状态.
- 计算了"总停留时间" (处于社会活动状态的时间),并使用回归模型来比较群体并与临床措施相关联.
主要成果:
- 与HC相比,在患有AD和记忆障碍的参与者中观察到总停留时间显著降低.
- 减少停留时间和AD之间的关联在HMM变异中是强大的.
- 对于患有精神分裂症的参与者来说,总停留时间没有显著差异.
结论:
- 隐藏的马尔科夫模型 (HMM) 提供了一个实用和可解释的数字表型化方法.
- 来自HMM的"总停留时间"作为一个客观的表型,表明社会功能.
- 这种方法有可能用于评估各种神经和精神疾病中的社会功能.
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