Informatics Methods for Leveraging Clinical Data Sources to Study Risk Factors for Alzheimer's Disease

项目摘要 电子健康记录（EHR）和医疗索赔数据等临床数据源有可能 作为一个巨大的研究资源，以支持目标，如确定可改变的风险因素， 阿尔茨海默病（AD），但临床数据具有显著的局限性，包括数据质量和缺失数据 挑战为了解决这些局限性，人们越来越关注将临床数据与研究联系起来 数据连接这些数据源有望协同研究质量的结果措施， 神经病理学数据，其中包含关于中年存在的潜在可改变的AD风险因素的丰富信息， 可以从临床数据中得出的共病状况和药物暴露。然而，整合 来自大型临床数据库的异质的、不一致的测量数据类型（例如，诊断代码， 处方药、影像学），在更小的、有针对性的研究中获得更一致的测量数据 人口需要发展新的信息学方法。 最近提出的深度学习方法有可能灵活地解释复杂的数据 临床数据中遇到的可用性模式，相对于传统的 方法.然而，下游分析的统计特性，如偏差和方差，还没有 在应用这些方法后进行了评估。此外，还需要专门的方法， 输入复杂数据类型，例如高维神经成像数据。除了解决 在临床数据衍生的数据集中，需要方法来促进组合来自 临床数据库和研究数据。 在这项研究中，我们建议通过以下方式来解决整合临床和研究数据库的挑战： 利用深度学习和迁移学习。我们将使用阿尔茨海默氏症患者的神经成像数据 来自成人思维变化研究的神经影像学倡议和队列研究数据， 来自Kaiser Permanente华盛顿EHR的临床数据，以开发新的信息学方法， 识别AD的危险因素。在目标1中，我们将开发一种用于数据集成的深度学习方法， 与研究数据相关的异质临床数据，解释了复杂的缺失数据模式 在临床数据中发现。在目标2中，我们将开发一个数据集成框架来支持迁移学习 从临床数据到研究数据，以推进有关AD风险因素的统计推断。很长的- 我们研究计划的长期目标是通过促进整合 异质收集的临床数据和研究数据，以利用各自的独特优势 数据源

Project Summary Clinical data sources such as Electronic Health Records (EHR) and medical claims data have the potential to serve as an enormous research resource to support goals such as identifying modifiable risk factors for Alzheimer’s Disease (AD), but clinical data have significant limitations including data quality and missing data challenges. To address these limitations, there is increasing interest in linking clinical data with research study data. Connecting these data sources promises to synergize research-quality outcome measures based on neuropathological data with rich information on potentially modifiable AD risk factors present in mid-life such as co-morbid conditions and medication exposures that can be derived from clinical data. However, integrating heterogeneous, inconsistently measured data types from a large clinical database (e.g., diagnosis codes, prescription medications, imaging) with more consistently measured data on a smaller, targeted study population requires development of novel informatics methodologies. Recently proposed deep learning approaches have the potential to flexibly account for the complex data availability patterns encountered in clinical data with improved predictive accuracy relative to traditional methods. However, statistical properties of downstream analyses, such as bias and variance, have not yet been evaluated following the application of these methods. Moreover, specialized methods are needed to impute complex data types such as high-dimensional neuroimaging data. In addition to addressing missingness within a clinical data-derived dataset, methods are needed to facilitate combining information from clinical databases and research study data. In this research, we propose to address the challenges of integrating clinical and research databases by harnessing deep learning and transfer learning. We will use neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative and cohort study data from the Adult Changes in Thought study in combination with clinical data from the Kaiser Permanente Washington EHR to develop novel informatics approaches to identification of risk factors for AD. In Aim 1, we will develop a deep learning approach to data integration for heterogeneous clinical data linked to research study data, accounting for complex missing data patterns encountered in clinical data. In Aim 2, we will develop a data integration framework to support transfer learning from clinical data to research study data to advance statistical inference about risk factors for AD. The long- term goal of our research program is to accelerate research on AD by facilitating integration of heterogeneously collected clinical data and research study data to capitalize on the unique strengths of each data source.