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

基本信息

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

项目摘要

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.

随着时间的流逝，由一组个人反复观察到的纵向数据在许多应用中很普遍，包括健康科学，学习科学，社会科学，生命科学和经济学。此类数据为揭示了某些测量变量（特征或协变量）的时间变化模式与感兴趣的结果之间的关系的前所未有的机会，例如，经济崩溃的社会动荡，疾病发作，健康风险，健康风险等。在现实世界中，变量的数量通常很大；通常，在任何给定时间都只记录一小部分变量，从而产生稀疏的数据，而缺失的观测值很高。此外，此类数据表现出复杂的相关性，如果未正确考虑，可能会导致误导性的统计推断。其他并发症是由于数据表现出突然的不连续性，而这些不连续性通常是由不直接观察到的状态之间的过渡驱动的（例如，从“健康”到“感染”）。大尺寸的数据集需求方法可扩展。在高利益应用中，例如医疗保健，人类对预测模型的解释性至关重要。该项目将在可扩展的机器学习方法中的当前最新方法中取得重大进展，以预测从高维，不规则地采样，稀疏，纵向健康数据的纵向结果进行预测。预测建模工具的开源实施将在许多领域中找到应用程序，包括行为，社会，环境，经济，学习和健康科学。该项目将增强对数据科学和计算机科学（尤其是人工智能）的不同研究生和本科生的研究培训，这是国家重要性的重要性。与该项目相关的教育活动将有助于配备各种数据科学家，人工智能专家以及健康科学，社会科学，学习科学及相关领域，并使用最先进的机器学习工具，用于从纵向数据中进行预测性建模。该项目将在预测性建模中生成新的研究生课程和课程模块，示例项目等，从纵向数据进行预测建模。该项目将有助于介绍来自不同背景的学生，包括妇女和代表性不足的少数民族，以了解数据科学领域的各种教育，研究和职业机会。通过广泛传播所有研究结果（出版物，软件，数据集，课程材料），该项目的更广泛影响将进一步增强。该项目将开发一个可伸缩的深层核心高斯流程流程回归算法，以从高度的高度模型中进行可解释的预测建模，稀疏和不规则的时间采样，具有复杂的纵向数据，这是先验未知的相关结构。最终的方法将能够发现未观察或隐藏状态之间的过渡模式，以说明结果中的突然不连续性。他们将能够通过学习数据所表现出的基本复杂相关结构，并不仅通过识别驱动预测的变量，还可以确定其这样做的时间环境来解释他们的预测。该项目将通过模拟纵向数据（具有不同的相关结构，不同的丢失机制，不同的时间依赖性可变重要性），几个基准测试纵向数据集以及最重要的是，最重要的是，更重要的是，更重要的是，除了纵向纵向的纵向电子健康记录数据和社会健康记录数据和社会， - 现实世界中医疗保健应用程序（与临床专家合作）的人口统计学数据。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。