CAREER: Interpretable Deep Modeling of Discrete Time Event Sequences

职业：离散时间事件序列的可解释深度建模

• 批准号: 1750326
• 负责人: Fei Wang
• 金额: $ 53.96万
• 依托单位: Joan and Sanford I. Weill Medical College of Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750326&HistoricalAwards=false
• 关键词: CAREER; Interpretable; Deep; Modeling; Discrete

Discrete Time Event Sequences (DTES) are ordered event sequences with a concrete timestamp associated with each event. DTES are ubiquitous in our daily life. One representative example is patient electronic health records. Computational modeling of DTES can reveal the hidden event evolving mechanisms and improve the performance of endpoint analytical tasks such as sequence forecasting and grouping. Conventional approaches for analyzing DTES are typically based on strong statistical assumptions and may not work well in practice. Motivated by the recent empirical success of deep learning methods in various application domains, the objective of this project is to develop interpretable deep learning approaches for modeling DTES. This project validates the utility of the developed algorithms in various medical applications. It incorporates the resulting research outcomes into curriculum development and courses, to train a new generation of machine learning and data mining practitioners. In addition, special training opportunities are provided to high school students and community college students for a broader education of modern data analysis techniques.This project consists of three synergistic research thrusts. First, it develops a series of approaches for integrating external domain knowledge into the modeling process. This guarantees the learned models align well with the domain knowledge and at the same time provides effective regularizations to avoid overfitting. Second, it devises approaches based on mimic learning and pattern dissection to interpret the knowledge hidden in the learned models. This makes the learned models much more practical and reusable. Third, effective model and data sharing mechanisms are developed to transfer the knowledge across similar learning tasks. This maximizes the utilizations of the available samples for each task by leveraging the task relationships. Two key problems in medical domain, hospital readmission and disease phenotyping, are used as the target applications for validating the proposed approaches based on several real-world large-scale patient electronic health record data sets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

离散时间事件序列（DTE）是与每个事件相关的具体时间戳的订购事件序列。 DTE在我们的日常生活中无处不在。一个代表性的例子是患者电子健康记录。 DTE的计算建模可以揭示隐藏事件不断发展的机制，并提高端点分析任务的性能，例如序列预测和分组。分析DTE的常规方法通常基于强烈的统计假设，并且在实践中可能无法很好地工作。由深度学习方法在各种应用领域的最新经验成功的推动下，该项目的目的是开发可解释的深度学习方法来建模DTE。该项目验证了在各种医学应用中开发算法的实用性。它将结果的研究成果纳入了课程开发和课程中，以培训新一代的机器学习和数据挖掘从业人员。此外，还向高中生和社区大学生提供了特殊的培训机会，以对现代数据分析技术进行更广泛的教育。该项目由三个协同的研究推力组成。首先，它开发了一系列用于将外部领域知识集成到建模过程中的方法。这确保了学识渊博的模型与域知识很好地保持一致，同时提供了有效的正规化以避免过度拟合。其次，它根据模仿学习和模式解剖来设计方法，以解释隐藏在学习模型中的知识。这使学习的模型更加实用和可重复使用。第三，开发了有效的模型和数据共享机制，以在相似的学习任务中转移知识。这通过利用任务关系来最大化每个任务的可用样本的利用。医疗领域，医院再入院和疾病表型的两个关键问题被用作基于几个现实世界中的大规模患者电子健康记录数据集验证所提出的方法的目标应用。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持。

项目成果

Uncovering Pattern Formation of Information Flow

• DOI: 10.1145/3292500.3330971
• 发表时间: 2019-07
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Chengxi Zang;Peng Cui;Chaoming Song;Wenwu Zhu;Fei Wang

Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

• DOI: 10.24963/ijcai.2018/483
• 发表时间: 2018-04
• 作者: Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang

Order-Preserving Metric Learning for Mining Multivariate Time Series

• DOI: 10.1109/icdm50108.2020.00080
• 发表时间: 2020-11
• 期刊: 2020 IEEE International Conference on Data Mining (ICDM)
• 作者: Jie Xu;Zhenxing Xu;Bin Yu;Fei Wang

Heterogeneous Hyper-Network Embedding

• DOI: 10.1109/icdm.2018.00104
• 发表时间: 2018-11
• 期刊: 2018 IEEE International Conference on Data Mining (ICDM)
• 作者: Inci M. Baytas;Cao Xiao;Fei Wang;Anil K. Jain;Jiayu Zhou

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19.

• DOI: 10.1038/s41598-021-95431-7
• 发表时间: 2021-08-05
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Su C;Xu Z;Hoffman K;Goyal P;Safford MM;Lee J;Alvarez-Mulett S;Gomez-Escobar L;Price DR;Harrington JS;Torres LK;Martinez FJ;Campion TR Jr;Wang F;Schenck EJ
• 通讯作者: Schenck EJ