Zhang Dongzhi, Lee Kyungmi, Lee Ickjai
Computer Science & Information Technology Academy, Division of Tropical Environments & Societies, James Cook University, Cairns, QLD 4870 Australia.
Health Inf Sci Syst. 2019 Apr 19;7(1):9. doi: 10.1007/s13755-019-0070-8. eCollection 2019 Dec.
Human movement behaviors could reveal many interesting medical patterns. Due to the advances in location-aware devices, a large volume of human movement behaviors has been captured in the form of spatio-temporal trajectories. These spatio-temporal trajectories are useful resources for medical data mining, and they could be used to classify which trajectory passes through medical centres and which one does not. Traditional approaches utilise time-series datasets while ignoring spatio-temporal semantics in order to detect periodic patterns in medical domains. They also fail to consider the inherent hierarchical nature of patterns. We investigate a medical data mining framework that generates multi-level medical periodic patterns. A Geolife dataset is used to test the feasibility and applicability of our framework. Experiments demonstrate that the proposed framework successfully distinguishes those who periodically visit medical centres from those who do not, and also to find multi-level medical periodic patterns revealing interesting hierarchical medical behaviours. One potential application includes an automated personalised medical service. For instance, medical institutions can send personalised relative medicine information to people who regularly visit certain medical centres. It will be useful for the discovery and diagnosis of diseases for patients.
人类运动行为能够揭示许多有趣的医学模式。由于位置感知设备的进步，大量人类运动行为已以时空轨迹的形式被捕获。这些时空轨迹是医学数据挖掘的有用资源，可用于对哪些轨迹经过医疗中心而哪些没有经过进行分类。传统方法利用时间序列数据集，同时忽略时空语义，以便在医学领域检测周期性模式。它们也未能考虑模式固有的层次性质。我们研究了一个生成多级医学周期性模式的医学数据挖掘框架。使用一个Geolife数据集来测试我们框架的可行性和适用性。实验表明，所提出的框架成功地区分了那些定期前往医疗中心的人和那些不去的人，并且还能找到揭示有趣层次医学行为的多级医学周期性模式。一个潜在应用包括自动化个性化医疗服务。例如，医疗机构可以向定期前往某些医疗中心的人发送个性化的相关医学信息。这将有助于患者疾病的发现和诊断。
