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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718696
• 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.

该项目旨在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。这项研究是我们应用的核心。在医疗保健领域，我们研究涉及风险因素的重复测量健康轨迹，以跟踪特定受试者在很长一段时间。我们的目的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。我们的目标是确定时间序列内的制度，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，例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。