TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting bias

TRiPOD：在 AD/ADRD 观察数据中实现可重复使用的表型 - 管理定义和纠正偏差

基本信息

批准号：
10279554
负责人：
Yong Chen
金额：
$ 81.17万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10279554
关键词：
Active Learning Address Affect Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease related dementia American Artificial Intelligence Cause of Death Clinical Research Communities Complex Data Data Analyses Deposition Disease Drug Exposure Electronic Health Record Engineering Epidemiologist Etiology Evaluation FAIR principles Goals Healthcare Incentives Incidence Informatics Investigation Joints Knowledge Literature Long-Term Care Medical Methods Modernization Observational Study Ontology Outcome Pathogenesis Patients Phenotype Prevention Protocols documentation Publication Bias Reproducibility Research Risk Factors Sampling Source Standardization Statistical Models Terminology Translational Research base care systems cohort community engagement data management effective therapy hospital services improved insight knowledge base novel payment phenotypic data phenotyping algorithm prevent repository risk stratification social tool trait treatment response web portal

项目摘要

Project Summary Large observational data such as electronic health records (EHRs) and medical claims have become an enabling source for facilitating clinical and translational research including Alzheimer's Disease and Alzheimer's Disease Related Dementia (AD/ADRD). One major challenge for conducting observational AD/ADRD studies is about phenotyping – there is a lack of a centralized repository for hosting and standardizing phenotype definitions in AD/ADRD research and few methods have been developed to address bias associated with phenotyping errors in observation data. Therefore, the overarching goal of this proposal is to fully develop a joint effort between medical informaticians, statisticians, clinicians, and epidemiologists with a focus on building a rigorous set of methods and tools for managing phenotype definitions and for correcting bias in observational data analysis, through modern knowledge engineering and data-driven statistical modeling. To achieve that goal, we propose three specific aims in this study: (1) Aim 1 - Collect, normalize, and share definitions of common phenotypes used in AD/ADRD observational research; (2) Aim 2 - Develop novel algorithms to correct bias associated with phenotyping errors when users apply existing phenotype definitions to local data; and (3) Aim 3 - Validate, refine, and disseminate proposed methods and tools by demonstration studies and community engagement. We believe informatics methods and tools proposed here will improve current practice on phenotypic data management and analysis, thus enhancing the reproducibility and quality of observational studies on AD/ADRD.

项目概要电子健康记录 (EHR) 和医疗索赔等大量观察数据已成为促进包括阿尔茨海默病在内的临床和转化研究的有利来源疾病和阿尔茨海默病相关的痴呆症 (AD/ADRD) 是一项重大挑战。进行AD/ADRD研究的观察性研究是关于表型分析——缺乏集中的用于托管和标准化 AD/ADRD 研究中表型定义的存储库已经开发出方法来解决与观察中表型错误相关的偏差因此，该提案的总体目标是充分发展各方之间的共同努力。医学信息学家、统计学家、人群和流行病学家，重点是建立一个一套严格的方法和工具，用于管理表型定义和纠正偏差通过现代知识工程和数据驱动的统计进行观察数据分析为了实现这一目标，我们在本研究中提出了三个具体目标：(1) 目标 1 - 收集，标准化并共享 AD/ADRD 观察中使用的常见表型的定义研究；(2) 目标 2 - 开发新的算法来纠正与表型错误相关的偏差当用户将现有表型定义应用于本地数据时；(3) 目标 3 - 验证、完善和通过示范研究和社区参与传播拟议的方法和工具。我们相信这里提出的信息学方法和工具将改善当前的实践表型数据管理和分析，从而提高重复性和质量 AD/ADRD 的观察性研究。