Large-scale Co-evolving Data Mining for Survival Event Prediction

这个项目的目的是研究协同进化序列数据的建模，并开发新的学习方法来预测长期的未来价值或事件。这项研究对我们的应用至关重要。在医疗保健领域，我们研究健康轨迹，包括重复测量风险因素，以在较长一段时间内跟踪特定受试者。我们的目标是预测临床失败事件，如死亡或再次住院。在金融领域，我们分析时间序列，包括每日历史股票报价、债券回报等的纵向轨迹。我们的目标是识别时间序列中的制度并预测制度变化，以帮助投资决策。在社会网络分析中，我们研究了动态在线网络的演化。我们的目标是识别用户之间的复杂关系，如相互影响和隐藏的亲和力；预测不良行为；并检测社区结构的变化。 该计划的长期目标是建立和验证框架，以有效地挖掘时空序列模式并生成预测性推理，同时为开发更具解释性的人工智能做出贡献。我们的短期目标是：(1)通过有意义的模式、轮廓和轨迹更好地表示协同进化序列数据；(2)对生态系统中协同进化序列之间的相互作用进行建模，以改进制度检测、制度变化预测和序列/事件预测；(3)研究离群点动力学，以更好地理解制度变化的驱动力；(4)开发有效的生存学习算法，用于从轨迹中学习和预测长期事件；以及(5)开发用于阐明决策过程的方法。 这一计划将通过完成六个相互关联的项目来实施。项目1专注于发现丰富的模式，这些模式可以用作表示共同发展轨迹的构建块。项目2和3旨在通过社区分析方法开发灵活的互动模型。为此，我们建议从轨迹的片段或窗口构建一个时间演化网络图，并研究网络中不断演化的社区结构。项目2是关于挖掘用户用电行为用于负荷预测，而项目3是关于挖掘制度事件用于时间序列预测。项目4是关于调查在个别轨迹或轨迹组的演变中导致结构性中断的离群值动态。项目5试图开发生存学习机器，探索重复测量协变量和无故障生存概率之间的潜在关系。最后，项目6从纵向数据的异类信息网络的角度研究生存分析，以便开发医疗轨迹应用程序。该计划将培训10名HQP。

This program aims to investigate the modelling of co-evolving sequential data and develop novel learning methods for predicting long-range future values or events. The research is central to our applications. In the healthcare domain, we study health trajectories involving repeated measures of risk factors to follow particular subjects over a prolonged period. Our aim is to predict clinical failure events such as death or rehospitalization. In the finance domain, we analyze time series comprising longitudinal trajectories of daily historical stock quotes, bond returns etc. Our aims are to identify regimes within the time series and predict regime changes to aid investment decision making. In social network analysis, we study the evolution of dynamic online networks. Our aims are to identify complex relationships between users, such as mutual influence and hidden affinities; to predict undesirable behaviours; and to detect changes in community structure. The long-term goal of this program is to build and validate frameworks to effectively mine spatiotemporal sequential patterns and generate predictive inferences while contributing to the development of more interpretable AI. Our short-term objectives are (1) to better represent co-evolving sequence data by meaningful patterns, profiles and trajectories; (2) to model interactions between co-evolving sequences in an ecosystem to improve regime detection, regime change prediction and sequence/event prediction; (3) to investigate outlier dynamics to better understand the driving forces for regime changes; (4) to develop effective survival learning algorithms for learning from trajectories and predicting long-range events; and (5) to develop methods for elucidating the decision process. This program will be carried out by accomplishing six interrelated projects. Project 1 focuses on discovering rich patterns that can be used as the building blocks for representing co-evolving trajectories. Projects 2 and 3 aim to develop flexible models of interactions via community analysis approaches. For this purpose, we propose to build a time-evolving network graph from segments or windows of trajectories, and investigate the evolving community structures within the network. Project 2 is about mining customers' electricity consumption behaviour for load forecasting, while Project 3 is about mining regime events for time series forecasting. Project 4 is about investigating outlier dynamics causing structural breaks in the evolution of individual trajectories or groups of trajectories. Project 5 attempts to develop survival-learning machines that explore the underlying relationship between repeated measures of covariates and failure-free survival probability. Finally, Project 6 investigates survival analysis from the perspective of a heterogeneous information network of longitudinal data in order to develop healthcare trajectory applications. This program will train 10 HQPs.