III: Small: Predictive Modeling from High-Dimensional, Sparsely and Irregularly Sampled, Longitudinal Data

III：小：根据高维、稀疏和不规则采样的纵向数据进行预测建模

• 批准号: 2226025
• 负责人: Vasant Honavar
• 金额: $ 59.99万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226025&HistoricalAwards=false
• 关键词: III; Small; Predictive; Modeling; Dimensional

Longitudinal data resulting from repeated observations from a set of individuals over time are commonplace in many applications, including health sciences, learning sciences, social sciences, life sciences, and economics. Such data present unprecedented opportunities to uncover the relationship between the time- varying patterns of certain measured variables (features or covariates) and outcomes of interest e.g., economic meltdown societal unrest, disease onset, health risk, etc. In real-world settings, the number of variables is often very large; often only a small subset of variables is recorded at any given time, resulting in sparse data with a high proportion of missing observations. Furthermore, such data exhibit complex correlations which if not properly accounted for, can lead to misleading statistical inferences. Additional complications arise from the fact that the data exhibit abrupt discontinuities that are often driven by transitions between states that are not directly observable (e.g., from "healthy" to "infected"). Large size of data sets demand methods that are scalable. And in high stakes applications, e.g., healthcare, human interpretability of the predictive models is of paramount importance. The project will yield substantial advances over the current state-of-the-art in scalable machine learning methods for predictive modeling of longitudinal outcomes from high-dimensional, irregularly sampled, sparse, longitudinal health data. The open-source implementations of the predictive modeling tools will find applications in many domains including behavioral, social, environmental, economic, learning, and health sciences. The project will enhance the research-based training of a diverse graduate and undergraduate students in Data Sciences and Computer Science (especially Artificial Intelligence), areas of great national importance. The educational activities associated with the project will help equip a diverse cadre of Data Scientists, AI experts, and health sciences, social sciences, learning sciences, and related areas with state-of-the-art machine learning tools for predictive modeling from longitudinal data. The project will produce a new graduate course and course modules, sample projects, etc. on predictive modeling from longitudinal data to be integrated into Data Sciences curricula. The project will help introduce students from diverse backgrounds, including women and underrepresented minorities, to a broad range of educational, research, and career opportunities in Data Sciences. The broader impacts of the project will be further enhanced by broad dissemination of all research results (publications, software, data sets, course materials).The project will develop a family of scalable deep kernel gaussian process regression algorithms for interpretable predictive modeling from high dimensional, sparsely and irregularly time sampled, longitudinal data with complex, a priori unknown correlation structure. The resulting methods will be able to discover the patterns of transitions between unobserved or hidden states, account for abrupt discontinuities in outcomes. They will be able to explain their predictions by learning the underlying complex correlation structure exhibited by the data and by identifying not only the variables that drive the predictions, but also the temporal context in which they do so. The project will rigorously empirically evaluate the resulting methods with simulated longitudinal data (with different correlation structures, different missingness mechanisms, different time-dependent variable importance), several benchmark longitudinal data sets, and, most importantly, deidentified longitudinal electronic health records data and socio-demographic data from real-world healthcare applications (in collaboration with clinical experts).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.

在许多应用中，包括健康科学、学习科学、社会科学、生命科学和经济学中，通过对一组个体在一段时间内的重复观察得出的纵向数据是很常见的。这些数据提供了前所未有的机会来揭示某些测量变量（特征或协变量）的时变模式与感兴趣的结果（如经济崩溃、社会动荡、疾病发作、健康风险等）之间的关系。在现实世界中，变量的数量通常非常大；通常，在任何给定的时间，只有一小部分变量被记录下来，导致数据稀疏，缺少观测值的比例很高。此外，这些数据显示出复杂的相关性，如果没有得到适当的解释，可能导致误导性的统计推断。另外的复杂情况是，数据表现出突然的不连续性，这种不连续性往往是由无法直接观察到的状态之间的过渡（例如，从“健康”到“感染”）造成的。大型数据集需要可扩展的方法。在高风险应用中，例如医疗保健，人类对预测模型的可解释性至关重要。该项目将在当前最先进的可扩展机器学习方法上取得实质性进展，用于从高维、不规则采样、稀疏的纵向健康数据中进行纵向结果的预测建模。预测建模工具的开源实现将在许多领域找到应用，包括行为、社会、环境、经济、学习和健康科学。该项目将加强数据科学和计算机科学（特别是人工智能）领域的研究生和本科生的研究培训，这些领域对国家具有重要意义。与该项目相关的教育活动将有助于为数据科学家、人工智能专家、健康科学、社会科学、学习科学和相关领域的各种骨干提供最先进的机器学习工具，用于从纵向数据中进行预测建模。该项目将制作一门新的研究生课程和课程模块、样本项目等，将纵向数据预测建模整合到数据科学课程中。该项目将帮助介绍来自不同背景的学生，包括女性和代表性不足的少数族裔，在数据科学领域获得广泛的教育、研究和职业机会。所有研究成果（出版物、软件、数据集、课程材料）的广泛传播将进一步加强该项目的广泛影响。该项目将开发一系列可扩展的深度核高斯过程回归算法，用于从高维，稀疏和不规则时间采样，具有复杂先验未知相关结构的纵向数据中进行可解释的预测建模。由此产生的方法将能够发现未观察或隐藏状态之间的转换模式，解释结果中的突然不连续。他们将能够通过学习数据所显示的潜在复杂的相关结构来解释他们的预测，并不仅通过识别驱动预测的变量，而且通过识别它们这样做的时间背景。该项目将通过模拟纵向数据（具有不同的相关结构、不同的缺失机制、不同的时变变量重要性）、几个基准纵向数据集，以及最重要的是，从真实医疗保健应用（与临床专家合作）中去识别的纵向电子健康记录数据和社会人口数据，对所得方法进行严格的实证评估。该奖项反映了美国国家科学基金会的法定使命，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

A Simple, Fast Algorithm for Continual Learning from High-Dimensional Data

一种简单、快速的高维数据持续学习算法

• 发表时间: 2023
• 期刊: Eleventh International Conference on Learning Representations
• 作者: Ashtekar, Neil;Honavar, Vasant G
• 通讯作者: Honavar, Vasant G