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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease care Alzheimer&apos s disease related dementia Alzheimer&apos 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

项目摘要

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.

项目概要/摘要候选人：Roy Adams 博士申请 K25 指导定量研究职业发展奖目标是作为一名专注于开发的方法论者，建立富有成效的独立研究生涯基于电子健康记录 (EHR) 的模型和工具可提高我们对阿尔茨海默病的了解 Adams 博士为患者带来了出色的计算方法培训。观察性健康数据，但缺乏 ADRD 方面的专业知识以及用于研究它们的方法。功能强大，但了解数据周围的上下文对于了解数据的局限性和 K25 培训将支持 Adams 博士成为一名独立的 ADRD 研究员。让他能够：(1) 了解痴呆症生物学和护理，(2) 获得方法方面的专业知识用于建立精神病学模型，(3) 从观察数据中获取 ADRD 研究的暴露测量值， (4) 形成临床 ADRD 研究合作者网络。这些培训目标将得以实现。通过面对面的临床接触、教学课程、定向阅读和期刊小组以及参与专业研究网络。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。错误的常见来源 - 例如分散的护理和选择偏差 - EHR 数据中 ADRD 的表型分析机器学习的最新进展提供了一种解释这些问题的潜在方法。通过组合感兴趣表型的多个代理来确定高维 EHR 数据中的偏差来源，虽然对每个代理中的错误和偏差进行了明确建模，但这些方法仍然有限且有限。在将其应用于 ADRD 数据之前，需要先进行方法开发，而不冒重大风险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 表型：二元表型，表明患者是否患有痴呆症和持续性痴呆亚当斯博士将把这些方法应用到一个大型数据库中，以测量痴呆症的严重程度。约翰霍普金斯大学的电子病历，并使用来自记忆中心的数据、来自并行正在进行的 ADRD 纵向研究，以及基于图表审查的患者严重程度评估。工作将利用约翰霍普金斯大学提供的独特资源组合阿尔茨海默病研究中心、里奇曼家庭精准医学卓越中心此外，这项研究还将促进阿尔茨海默病和约翰霍普金斯大学健康精准医学计划。为 Adams 博士提供处理 ADRD 患者数据的宝贵经验，为未来奠定基础方法论工作，并生成可直接应用于多个计划和正在进行的方法约翰霍普金斯大学的 ADRD 精准医学研究。