A unified longitudinal functional data framework for the analysis of complex biomedical data

用于分析复杂生物医学数据的统一纵向功能数据框架

• 批准号: 9118239
• 负责人: Damla Senturk
• 金额: $ 32.6万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9118239
• 关键词: Address; Algorithms; Area; Bayesian Analysis; Big Data; Biomedical Engineering; Brain; Cardiac; Cardiopulmonary Arrest; Cerebrum; Characteristics; Child; Classification; Clinical; Collaborations; Communities; Complex; Computerized Medical Record; Data; Data Analyses; Data Collection; Data Reporting; Data Science; Data Set; Decision Making; Development; Diagnosis; Discipline of Nursing; E-learning; Electrocardiogram; Electroencephalogram; Elements; Environment; Estimation Techniques; Event; Event-Related Potentials; Evidence Based Medicine; Evolution; Foundations; Frequencies; Goals; Health; Healthcare; Image; Individual; Intensive Care Units; Investigation; Literature; Measurement; Measures; Medical; Methodology; Methods; Modeling; Monitor; Nature; Neurologic; Neurology; Patient Monitoring; Patients; Physiologic Monitoring; Physiological; Procedures; Process; Property; Psychiatry; Public Health Applications Research; Reading; Regression Analysis; Research; Running; Signal Transduction; Solid; Source; Statistical Algorithm; Statistical Methods; Statistical Models; Structure; Survival Analysis; Techniques; Time; Traumatic Brain Injury; Variant; Visit; Work; autism spectrum disorder; base; computer science; computerized tools; density; disorder subtype; evidence base; flexibility; follow-up; functional outcomes; indexing; information framework; innovation; longitudinal analysis; novel; pressure; prospective; statistics; theories; tool

 DESCRIPTION (provided by applicant): The continuous evolution in our ability to measure and record complex biomedical data has opened new opportunities, as well as new challenges in the development of evidence based medical care and health management. This application is concerned with complex patient level information, acquired in the form of high frequency functional data (ECG signals, cerebral environment monitors, images, etc.) recorded over several visits or in the setting of medium to long periods of intensive physiological monitoring (for example, Intensive Care Unit settings). We conceptually characterize this information framework as longitudinal functional data. This involves representation of these data classes, in relation to two time scales: historical time, indexing long term changes in the dynamic of processes under investigation, and clock time, indexing short term dynamics. This characterization achieves the goals of identifying sources of variation in the data that are readil interpretable for scientific investigation. We propose a comprehensive holistic development of the theory and methodology for the analysis of longitudinal functional data that span the subjects of regression, clustering and classification and dynamic computation. These developments will provide interpretation and rigorous inference to help guide health care decisions based on complex biomedical data. Even though longitudinal and functional data analysis have established solid bodies of theory and methods, current literature does not yet address analysis of longitudinal functional data with multiple covariates under flexible assumptions. In addition, most applications in the functional data analysis literature involve analysis of data in relatively short periods of time and methods are not directly applicable to medium to long periods of intensive physiological monitoring settings. We propose to analyze these larger scale data sets by the proposed novel longitudinal functional data framework involving chunking longer periods of follow-up into longitudinal units. This is a novel idea in thi literature which utilizes both longitudinal and functional data analysis tools to achieve data analysis in a new level of data complexity. A second element of innovation in our application will consist in the development of fast and accurate algorithms for statistical inference in real time, which will make our methodological contribution ever more useful for clinical and public health applications. We propose three Specific Aims: 1) To develop statistical methods for regression analysis and prediction in the setting of longitudinal functional data; 2) To develop clustering an classification methodology for longitudinal functional data; 3) To develop fast and feasible estimation techniques aimed at online learning in high dimensional settings. These three Aims are accompanied by a fourth exploratory Aim, where we propose to develop statistical methods for time to event analysis using longitudinal functional predictors. Applications for the proposed methodology will include Intensive Care Unit data on traumatic brain injury and cardiopulmonary arrest patients and ERP data in autism spectrum disorder studies.

 描述(由申请人提供)：我们测量和记录复杂生物医学数据的能力不断发展，为循证医疗和健康管理的发展打开了新的机遇，也带来了新的挑战。该应用程序涉及以高频功能数据(心电信号、脑环境监视器、图像等)的形式获取的复杂患者级别信息。记录在几次访问中或在中到长时间的密集生理监测(例如，重症监护病房设置)中。我们在概念上将这一信息框架描述为纵向功能数据。这涉及用两个时标表示这些数据类别：历史时间，索引所调查过程的动态的长期变化；时钟时间，索引短期动态。这一特征实现了在数据中找出可供科学调查解释的变异来源的目标。我们提出了一个全面的整体发展的理论和方法的纵向函数数据的分析涵盖了回归，聚类和分类和动态计算的主题。这些发展将提供解释和严格的推论，以帮助指导基于复杂生物医学数据的卫生保健决策。尽管纵向和函数数据分析已经建立了坚实的理论和方法体系，但目前的文献还没有在灵活的假设下处理具有多个协变量的纵向函数数据的分析。此外，功能数据分析文献中的大多数应用涉及在相对较短的时间段内分析数据，并且方法不能直接适用于中到长时间段的密集生理监测设置。我们建议通过提出的新的纵向函数数据框架来分析这些更大规模的数据集，该框架涉及将较长时间的随访分成纵向单元。这是这篇文献中的一个新想法，它利用纵向和功能性数据分析工具来实现在新的数据复杂性水平上的数据分析。我们应用程序的第二个创新元素将在于开发用于实时统计推断的快速和准确的算法，这将使我们的方法学贡献对临床和公共卫生应用程序更加有用。我们提出了三个具体目标：1)开发用于纵向函数数据的回归分析和预测的统计方法；2)开发用于纵向函数数据的分类方法的聚类法；3)开发针对高维环境下在线学习的快速可行的估计技术。这三个目标伴随着第四个探索性目标，在那里我们建议开发统计方法，使用纵向功能预测因子进行时间到事件的分析。拟议方法的应用将包括重症监护病房关于创伤性脑损伤和心肺骤停患者的数据，以及自闭症谱系障碍研究中的ERP数据。