用两阶段模型分析痴呆患者日常活动，挖掘行为模式与偏差
Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
- 先将时间序列转为文本，再用语言模型编码
- 通过二维化与PageRank分析行为状态转移，量化活动偏好
- 适合关注个性化远程护理的医疗研究者
在远程健康监测数据分析中，时间序列表征学习能有效揭示患者行为的深层模式，尤其适用于高时间粒度的数据。本研究聚焦于痴呆患者居家活动记录数据集，提出一种两阶段自监督学习方法。第一阶段将时间序列活动转化为文本字符串，并由微调的语言模型进行编码；第二阶段对这些时间序列向量进行二维化处理，应用PageRank算法分析潜在状态转移，定量评估参与者的行为模式并识别活动偏差。结合诊断数据，该方法旨在支持个性化照护干预。
原文摘要 · Abstract (English)
In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially given the fine temporal granularity of the data. In this study, we focus on a dataset of home activity records from people living with Dementia. We propose a two-stage self-supervised learning approach. The first stage involves converting time-series activities into text strings, which are then encoded by a fine-tuned language model. In the second stage, these time-series vectors are bi-dimensionalized for applying PageRank method, to analyze latent state transitions to quantitatively assess participants behavioral patterns and identify activity biases. These insights, combined with diagnostic data, aim to support personalized care interventions.
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