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.

项目摘要电子健康记录（EHR）和医疗主张等大型观察数据成为促进临床和翻译研究的促进来源，包括阿尔茨海默氏症疾病和阿尔茨海默氏病有关的痴呆症（AD/ADRD）。一个主要挑战进行观察性AD/ADRD研究是关于表型的 - 缺乏集中式用于托管和标准化AD/ADRD研究中表型定义的存储库，很少已经开发了解决与观察表型误差相关的偏见的方法数据。因此，该提案的总体目标是完全努力医学信息，统计学家，临床医生和流行病学家，重点是建造严格的一组方法和工具来管理表型定义和纠正偏见通过现代知识工程和数据驱动的统计数据观察数据分析造型。为了实现这一目标，我们在这项研究中提出了三个具体目标：（1）目标1-收集，正常化，并共享AD/ADRD观察性中使用的常见表型的定义研究; （2）目标2-开发新型算法以纠正与表型误差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，精炼和通过演示研究和社区参与来传播建议的方法和工具。我们认为，这里提出的信息方法和工具将改善当前的实践表型数据管理和分析，从而提高了可重复性和质量关于AD/ADRD的观察性研究。