Machine learning-based methods for phenotyping dementia patients from electronic health record data

基于机器学习的方法，根据电子健康记录数据对痴呆症患者进行表型分析

• 批准号: 10720916
• 负责人: Roy Adams
• 金额: $ 13.84万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720916
• 关键词: Address; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease care; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Big Data; Biological; Biology; Calibration; Caring; Clinical; Code; Complex; Computer software; Computing Methodologies; Data; Databases; Dementia; Development; Diagnosis; Disease; Electronic Health Record; Environment; Equity; Error Sources; Exposure to; Family; Foundations; Funding; Future; Goals; Heterogeneity; Journals; K-Series Research Career Programs; Lead; Learning; Longitudinal Studies; Machine Learning; Measurement; Measures; Memory; Mentors; Methodology; Methods; Modeling; Outcome; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Persons; Pharmaceutical Preparations; Phenotype; Precision Medicine Initiative; Productivity; Proxy; Psychiatric epidemiology; Public Health; Research; Research Personnel; Resources; Risk; Risk Factors; Sample Size; Selection Bias; Severities; Source; Time; Training; United States National Institutes of Health; Validity and Reliability; Work; barrier to care; care fragmentation; career; clinical development; cohort; computing resources; cost; dementia care; detection method; diagnosis standard; disease heterogeneity; experience; geriatric neuropsychiatry; health care availability; health data; high dimensionality; high risk; improved; interest; large scale data; machine learning method; model building; open source; patient population; patient subsets; precision medicine; repository; skills; supervised learning; tool; treatment center

PROJECT SUMMARY/ABSTRACT Candidate: Dr. Roy Adams applies for this K25 Mentored Quantitative Research Career Development Award with the goal of building a productive independent research career as a methodologist focused on developing electronic health record (EHR)-based models and tools to improve our understanding of Alzheimer’s disease and related dementias (ADRDs). Dr. Adams brings with him excellent training in computational methods for observational health data but lacks expertise in ADRDs and the methods used to study them. “Big data” is powerful but understanding the context surrounding the data is essential for knowing the limits of the data and avoiding bias. The K25 training will support Dr. Adams in becoming an independent ADRD researcher by allowing him to: (1) develop an understanding of dementia biology and care, (2) gain expertise in the methods used to model psychiatric measurements, (3) gain exposure to the study of ADRDs from observational data, and (4) form a network of collaborators in clinical ADRD research. These training aims will be accomplished through in-person clinical exposure, didactic courses, directed readings and journal groups, and participation in professional research networks. Research and Environment: Phenotyping is an essential step of most EHR-based studies of ADRDs. Due to common sources of error – such as fragmented care and selection bias – phenotyping ADRDs in EHR data remains a challenge. Recent advances in machine learning present a potential way to account for these sources of bias in high-dimensional EHR data by combining multiple proxies for the phenotype of interest, while explicitly modeling the error and bias in each proxy. However, these methods remain limited and methodological development is needed before they can be applied to ADRD data without risking substantial bias. The proposed research focuses on developing these methods to extract two types of EHR-based phenotypes of ADRD: a binary phenotype indicating whether a patient has dementia and a continuous phenotype measuring the severity of that dementia. Dr. Adams will apply these methods to a large database of Johns Hopkins EHRs and validate them using a combination of data from a memory center, data from a parallel ongoing longitudinal study of ADRDs, and assessments of patient severity based on chart review. This work will take advantage of a unique combination of resources available through the Johns Hopkins Alzheimer’s Disease Research Center, the Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, and the Johns Hopkins inHealth Precision Medicine initiative. Further, this research will provide Dr. Adams with valuable experience working with ADRD patient data, set the foundation for future methodological work, and generate methods that can be directly applied to several planned and ongoing ADRD precision medicine studies at Johns Hopkins.

项目摘要/摘要 候选人：罗伊亚当斯博士申请这个K25指导定量研究职业发展奖 目标是建立一个富有成效的独立研究生涯，作为一个专注于发展的方法学家， 基于电子健康记录（EHR）的模型和工具，以提高我们对阿尔茨海默病的理解 和相关痴呆症（ADRD）。亚当斯博士带来了他在计算方法方面的出色训练， 观察性健康数据，但缺乏ADRD的专业知识和研究方法。“大数据”是 功能强大，但了解数据的上下文对于了解数据的局限性至关重要， 避免偏见。K25培训将支持亚当斯博士成为一名独立的ADRD研究人员， 使他能够：（1）发展对痴呆生物学和护理的理解，（2）获得方法方面的专业知识 用于对精神病学测量进行建模，（3）从观察数据中了解ADRD研究， 形成ADRD临床研究的合作者网络。这些培训目标将得以实现 通过亲自临床接触，教学课程，指导阅读和期刊小组，并参与 专业研究网络。 研究和环境：表型分析是大多数基于EHR的ADRD研究的重要步骤。由于 常见的错误来源-例如分散的护理和选择偏倚-EHR数据中的ADRD表型 仍然是一个挑战。机器学习的最新进展为解决这些问题提供了一种潜在的方法。 通过结合感兴趣的表型的多个代理， 同时明确地对每个代理中的误差和偏差进行建模。然而，这些方法仍然有限， 在将其应用于ADRD数据而不冒重大风险之前，需要进行方法学开发。 bias.建议的研究重点是开发这些方法来提取两种类型的EHR为基础的 ADRD的表型：指示患者是否患有痴呆的二元表型和连续的 衡量痴呆严重程度的表型。亚当斯博士将把这些方法应用于一个大型数据库， 约翰霍普金斯EHR和验证他们使用的数据组合从记忆中心，数据从 ADRD的平行持续纵向研究，以及基于病历审查的患者严重程度评估。这 这项工作将利用约翰霍普金斯大学独特的资源组合 阿尔茨海默病研究中心，Richman家族精准医学卓越中心， 阿尔茨海默病，以及约翰霍普金斯健康精准医学倡议。此外，这项研究将 为亚当斯博士提供了处理ADRD患者数据的宝贵经验，为未来的 方法工作，并产生可以直接应用于几个计划和正在进行的方法 约翰霍普金斯的ADRD精准医学研究。