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 作为支持目标的巨大研究资源，例如确定可修改的风险因素 阿尔茨海默病(AD)，但临床数据有很大限制，包括数据质量和缺失数据 挑战。为了解决这些局限性，人们越来越有兴趣将临床数据与研究研究联系起来 数据。将这些数据源连接起来有望基于以下方面协同研究质量成果衡量标准 关于中年存在的潜在可修改AD风险因素的丰富信息的神经病理数据，例如 可以从临床数据中得出的共病情况和药物暴露。然而，集成 来自大型临床数据库的异质、不一致测量的数据类型(例如，诊断代码， 处方药，成像)，在较小的目标研究中有更一致的测量数据 人口需要发展新的信息学方法。 最近提出的深度学习方法具有灵活地处理复杂数据的潜力 与传统数据相比，临床数据中遇到的可用性模式具有更高的预测准确性 方法：研究方法。然而，下游分析的统计特性，如偏差和方差，还没有 在应用这些方法之后进行了评估。此外，还需要专门的方法来 归因于复杂的数据类型，例如高维神经成像数据。除了寻址 在临床数据派生数据集中的缺失，需要方法来促进将来自 临床数据库和研究研究数据。 在这项研究中，我们建议通过以下方式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病的神经成像数据 结合成人思维变化研究的神经影像主动性和队列研究数据 来自Kaiser Permanente Washington EHR的临床数据开发新的信息学方法 AD危险因素的识别。在目标1中，我们将为数据集成开发一种深度学习方法 链接到研究研究数据的异质临床数据，解释了复杂的缺失数据模式 在临床数据中遇到。在目标2中，我们将开发一个支持迁移学习的数据集成框架 从临床数据到研究性研究数据，推进AD危险因素的统计推断。长的- 我们研究计划的长期目标是通过促进 以不同的方式收集临床数据和研究研究数据，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.