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（Roy Adams）博士申请这一K25指导的定量研究职业发展奖 作为一名专注于发展的方法学家，建立富有生产力的独立研究职业 电子健康记录（EHR）的模型和工具，以提高我们对阿尔茨海默氏病的理解 和相关痴呆症（ADRDS）。亚当斯博士为他带来了出色的计算方法培训 观察性健康数据，但缺乏ADRD的专业知识以及用于研究它们的方法。 “大数据”是 强大但了解数据围绕数据的上下文对于了解数据的限制至关重要 避免偏见。 K25培训将支持亚当斯博士成为一名独立的ADRD研究员 允许他：（1）对痴呆症生物学和护理的了解，（2）在方法中获得专业知识 用于建模精神病学测量，（3）从观察数据中获得对ADRD的研究， （4）组成了临床ADRD研究中的合作者网络。这些训练目标将实现 通过面对面的临床暴露，教学课程，定向阅读和期刊组以及参与 专业研究网络。 研究与环境：表型是大多数基于EHR的ADRDS研究的重要步骤。由于 常见的错误来源 - 例如零散的护理和选择偏见 - EHR数据中的表型ADRD 仍然是一个挑战。机器学习的最新进展提出了一种解决这些问题的潜在方法 高维EHR数据中偏差的来源，通过结合感兴趣表型的多个代理， 同时明确对每个代理中的误差和偏差进行建模。但是，这些方法仍然有限， 在将它们应用于ADRD数据的情况下，需要进行方法论开发而不冒险 偏见。拟议的研究重点是开发这些方法来提取两种基于EHR的类型 ADRD的表型：一种二进制表型，表明患者是否患有痴呆症和连续 测量该痴呆症的严重程度的表型。亚当斯博士将把这些方法应用于大型数据库 Johns Hopkins EHRS并使用内存中心的数据组合验证它们 根据图表审查，对ADRD的持续纵向研究以及患者严重程度的评估。这 工作将利用约翰·霍普金斯（Johns Hopkins）可用的独特资源组合 阿尔茨海默氏病研究中心，里奇曼家族精密医学卓越中心 阿尔茨海默氏病和约翰·霍普金斯（John Hopkins）Inhealth Precision Medicine Initiative。此外，这项研究将 为亚当斯博士提供有价值的经验，可与ADRD患者数据一起工作，为未来奠定基础 方法论工作，并生成可以直接应用于几种计划和持续的方法 约翰·霍普金斯（Johns Hopkins）的Adrd Precision医学研究。