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

许多人类事件，如亲临医院、流感暴发或抗议活动，都是按时间顺序记录的，并呈现反复出现的模式。例如，在住院记录中，被诊断为高血压的患者往往后来因心脏病去医院就诊。使用过去的事件模式预测人类事件对许多利益相关者来说是人工智能辅助决策的关键。可解释的预测模型将显著提高这些决策过程的透明度。近年来，可解释机器学习引起了越来越多的关注。然而，这一领域的大多数最先进的工作都集中在静态分析上，例如识别图像中的目标检测像素。对于动态、异质和多源数据序列中的时间事件预测，很少有人开展工作。为了解决这一问题，该项目将支持为具有异质和多源特征的不同范围的时间事件序列设计可转换的可解释范例。提供能够捕获分层的、关系的和复杂的证据的预测工具将丰富和支持未来的可靠预测。这项工作将涉及教育活动，如开发关于可解释的机器学习的新课程；培训研究生、本科生和高中生进行跨学科研究；增加妇女和少数群体对学术研究的参与。该项目的核心成果，如软件、数据集和出版物，将向公众开放。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、异质和多源的解释。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，但仍有几个独特的挑战尚未被探索：(1)鉴于注意力机制在深度学习中的广泛采用，规范基于注意力的模型来审计模型是迫切需要的。(2)目前的方法大多侧重于基于相关性来选择重要的输入特征，而这些相关性往往缺乏因果证据。(3)目前的研究大多忽略了异质数据源之间的相互关系和依赖关系。本项目将通过以下方式应对这些挑战：(I)将利用领域知识进行校准，研究新的协作注意调节策略。(Ii)将动态因果发现与具有隐藏混乱器表征学习的时间事件预测相结合。(3)它将通过从非结构化文本中提取语义知识并将这些知识纳入与多源时态数据的共同学习框架中，提供多方面的解释。具体的研究目标将得到一套广泛的评价计划的补充，包括对多范围真实世界事件记录的标准回溯性评价，以及评估所开发模型的可解释性的多用户研究。项目成果包括观测数据、可解释的预测工具和利益相关者的开源软件，将与计算机科学研究社区和医疗保健、政治学和流行病学的其他从业人员共享。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.