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

用于生存事件预测的大规模协同进化数据挖掘

• 批准号: RGPIN-2020-07110
• 负责人: Wang, Shengrui
• 金额: $ 3.5万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741599
• 关键词: Large; scale; Co; evolving; Data

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.

该计划旨在研究共同进化的序列数据的建模，并开发新的学习方法来预测长期的未来值或事件。研究是我们应用的核心。在医疗保健领域，我们研究健康轨迹，涉及风险因素的重复测量，以长期跟踪特定受试者。我们的目标是预测临床失败事件，如死亡或再住院。在金融领域，我们分析包括每日历史股票报价，债券收益率等纵向轨迹的时间序列，我们的目标是识别时间序列内的制度，并预测制度变化，以帮助投资决策。在社会网络分析中，我们研究动态在线网络的演变。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构建和验证框架，以有效地挖掘时空序列模式并生成预测推理，同时为开发更具可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共同进化的序列数据;（2）模拟生态系统中共同进化序列之间的相互作用，以改善状态检测，状态变化预测和序列/事件预测;（3）调查离群动态，以更好地理解状态变化的驱动力;（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;（5）开发阐明决策过程的方法。该计划将通过完成六个相互关联的项目来实施。项目1的重点是发现丰富的模式，可用作表示共同进化轨迹的构建块。项目2和3旨在通过社区分析方法开发灵活的互动模式。为此，我们建议从轨迹的片段或窗口构建一个随时间演化的网络图，并研究网络中不断演化的社区结构。项目2是关于挖掘客户的电力消耗行为用于负荷预测，而项目3是关于挖掘状态事件用于时间序列预测。项目4是关于调查离群动态导致结构性断裂的演变，个别轨迹或轨迹组。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从纵向数据的异构信息网络的角度研究生存分析，以开发医疗保健轨迹应用程序。该方案将培训10名HQP。