CAREER: Towards Deep Interpretable Predictions for Multi-Scope Temporal Events

职业：对多范围时间事件进行深度可解释的预测

• 批准号: 2047843
• 负责人: Yue Ning
• 金额: $ 57.19万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047843&HistoricalAwards=false
• 关键词: CAREER; Towards; Deep; Interpretable; Predictions

Many human events, such as personal visits to hospitals, flu outbreaks, or protests, are recorded in temporal sequences and exhibit recurring patterns. For instance, in hospital admission records, patients who have been diagnosed with hypertension often later visit the hospital for heart diseases. Predictions of human events using past event patterns are key to many stakeholders in AI-assisted decision making. Interpretable predictive models will significantly improve transparency in these decision-making processes. Recently, interpretable machine learning has been drawing an increasing amount of attention. However, most state-of-the-art works in this domain focus on static analysis such as identifying pixels for object detection in an image. Little work has been developed for temporal event prediction in dynamic, heterogeneous, and multi-source data sequences. To address this problem, this project will support the design of transformative interpretable paradigms for temporal event sequences of different scopes with heterogeneous and multi-source features. Providing predictive tools that can capture hierarchical, relational, and complex evidence will enrich and support robust forecasting in the future. This work will involve educational activities such as developing new courses on interpretable machine learning; training graduate, undergraduate, and high-school students in interdisciplinary studies; and increasing participation of women and minority groups in academic research. Core outcomes of this project such as software, datasets, and publications will be made available to the general public.This project will create a new set of interpretable mechanisms that provide dynamic, heterogeneous, and multi-source explanations in temporal event prediction. Although a variety of explainable approaches have been developed in many traditional machine learning tasks, several unique challenges remain unexplored: (1) Regulating attention-based models for auditing a model is an urgent need given the wide adoption of attention mechanisms in deep learning. (2) Most current approaches focus on selecting important input features based on correlations which often lack causal evidence. (3) Reciprocal relations and dependencies among heterogeneous data sources are largely ignored in current research. This project will address these challenges in the following ways: (i) It will investigate new collaborative attention regulation strategies by using domain knowledge for calibration. (ii) It will integrate dynamic causal discovery into temporal event prediction with hidden confounder representation learning. (iii) It will provide multi-faceted explanations by distilling semantic knowledge from unstructured text and incorporating this knowledge in a co-learning framework with multi-source temporal data. The specific research aims will be complemented by an extensive set of evaluation plans including standard retrospective evaluation on multi-scope real-world event records as well as multiple user studies to evaluate the interpretability of developed models. The project outcomes including observational data, interpretable prediction tools, and open-source software for stakeholders will be shared with the computer science research community and other practitioners in healthcare, political science, and epidemiology.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.

许多人类事件，如个人访问医院，流感爆发或抗议，都是以时间序列记录的，并表现出重复的模式。例如，在入院记录中，被诊断患有高血压的患者往往后来因心脏病前往医院。使用过去的事件模式预测人类事件是人工智能辅助决策中许多利益相关者的关键。可解释的预测模型将大大提高这些决策过程的透明度。最近，可解释的机器学习引起了越来越多的关注。然而，大多数国家的最先进的作品在这一领域的重点是静态分析，如识别像素的图像中的目标检测。在动态、异构和多源数据序列中，时间事件预测的工作很少。为了解决这个问题，这个项目将支持的异构和多源功能的不同范围的时间事件序列的转换可解释范式的设计。提供能够捕捉层次、关系和复杂证据的预测工具将丰富和支持未来的稳健预测。这项工作将涉及教育活动，例如开发可解释机器学习的新课程;培训研究生，本科生和高中生进行跨学科研究;以及增加妇女和少数群体参与学术研究。该项目的核心成果，如软件，数据集和出版物将提供给公众。该项目将创建一套新的可解释机制，在时间事件预测中提供动态，异构和多源解释。尽管在许多传统的机器学习任务中已经开发了各种可解释的方法，但仍有一些独特的挑战尚未探索：（1）鉴于深度学习中广泛采用注意力机制，迫切需要调节基于注意力的模型来审核模型。(2)目前大多数方法都侧重于选择重要的输入功能的基础上的相关性，往往缺乏因果关系的证据。(3)异构数据源之间的相互关系和依赖性在目前的研究中被很大程度上忽略了。该项目将通过以下方式解决这些挑战：（i）通过使用领域知识进行校准，研究新的协作注意力调节策略。(ii)它将动态因果发现与隐藏混淆表征学习集成到时间事件预测中。(iii)它将通过从非结构化文本中提取语义知识，并将这些知识与多源时态数据结合在一个共同学习框架中，提供多方面的解释。具体的研究目标将得到一套广泛的评估计划的补充，包括对多范围真实世界事件记录的标准回顾性评估以及多个用户研究，以评估开发模型的可解释性。该项目的成果包括观测数据、可解释的预测工具和面向利益相关者的开源软件，将与计算机科学研究社区以及医疗保健、政治学和流行病学领域的其他从业者分享。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

FairLP: Towards Fair Link Prediction on Social Network Graphs

FairLP：迈向社交网络图上的公平链接预测

• DOI: 10.1609/icwsm.v16i1.19321
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Li, Yanying;Wang, Xiuling;Ning, Yue;Wang, Hui
• 通讯作者: Wang, Hui

Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

• DOI: 10.1609/aaai.v36i4.20380
• 发表时间: 2021-12
• 期刊: ArXiv
• 作者: Chang Lu;Tian Han;Yue Ning

Algorithmic fairness in computational medicine.

计算医学中的算法公平性。

• DOI: 10.1016/j.ebiom.2022.104250
• 发表时间: 2022-10
• 期刊: EBIOMEDICINE
• 影响因子: 11.1
• 作者: Xu, Jie;Xiao, Yunyu;Wang, Wendy Hui;Ning, Yue;Shenkman, Elizabeth A.;Bian, Jiang;Wang, Fei
• 通讯作者: Wang, Fei

Robust Event Forecasting with Spatiotemporal Confounder Learning

• DOI: 10.1145/3534678.3539427
• 发表时间: 2022-08
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Songgaojun Deng;H. Rangwala;Yue Ning

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

• DOI: 10.1109/tcyb.2021.3109881
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Cybernetics
• 影响因子: 11.8
• 作者: Chang Lu;Chandan K. Reddy;Yue Ning