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Updated: Jul 26, 2025
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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预测遇到时间序列的空白:用户集群具有特定的使用行为模式
Miro Schleicher1, Vishnu Unnikrishnan1, Rüdiger Pryss2
1Knowledge Management & Discovery Lab, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
Artificial intelligence in medicine
|June 14, 2023
概括
这项研究引入了一种分析用户参与移动健康 (mHealth) 应用程序的新方法. 它有助于预测用户学率和识别坚持模式,改善治疗数据分析.
科学领域:
- 数字健康数字健康
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 移动健康 (mHealth) 应用程序为治疗收集真实世界的数据,但受到波动的参与和高用户学率的影响.
- 这些数据挑战阻碍了机器学习 (ML) 分析和对用户坚持的理解.
- 识别用户脱离参与对于有效的移动健康干预至关重要.
研究的目的:
- 开发一种方法来识别和预测mHealth应用数据集中的不同脱学率.
- 根据用户当前的参与状态来预测用户不活动的时间.
- 分析不同用户集群中坚持的演变.
主要方法:
- 利用变化点检测来识别用户参与和弃的不同阶段.
- 采用时间序列分类来预测基于用户活动的用户阶段.
- 解决了缺乏值的不均,不整齐的时间序列的挑战.
- 在mHealth应用程序数据集上评估了用于 tinnitus 管理的方法.
主要成果:
- 成功识别了不同学率的阶段,并预测了未来的用户行为.
- 证明了预测个人用户预期不活动时间的能力.
- 展示了不同用户集群的坚持演变.
- 在真实世界的mHealth数据上验证了该方法的有效性.
结论:
- 拟议的方法有效地处理在mHealth应用程序中常见的不均,不对齐的时间序列数据.
- 这种方法适用于研究缺失值和可变长度的数据集中的用户坚持.
- 这些发现可以提高数据分析和干预策略在mHealth的可靠性.
- 准确预测用户退出和坚持是优化数字健康工具至关重要的.
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