An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health records

使用统一的电子健康记录研究多种慢性病 (MCC) 对阿尔茨海默病的影响的端到端信息学框架

• 批准号: 10728800
• 负责人: Jiang Bian
• 金额: $ 116.82万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10728800
• 关键词: African American population; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease risk; Biological Markers; Cardiovascular Diseases; Caucasians; Characteristics; Chronic Disease; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Data; Clinical Pathways; Clinical Research; Complex; Consent; Data; Data Coordinating Center; Databases; Dementia; Development; Diagnosis; Disease Progression; Electronic Health Record; Eligibility Determination; Ethnic Population; Fast Healthcare Interoperability Resources; Florida; Health; Health system; Healthcare; Impaired cognition; Incidence; Informatics; Intervention Trial; Knowledge; Link; Maps; Medical; Mental Depression; Methods; Modeling; Natural Language Processing; Nature; Obstructive Sleep Apnea; Onset of illness; Ontology; Outcome; Participant; Patients; Pattern; Performance; Periodontitis; Phenotype; Prevalence; Process; Public Health; Records; Research; Research Personnel; Risk; Risk Factors; Standardization; Structure; System; Testing; Work; World Health; aging population; application programming interface; burden of illness; care costs; care seeking; cerebral atrophy; cognitive benefits; cognitive testing; cognitive training; cohort; comorbidity; data format; data standards; deep learning; efficacy evaluation; electronic health record system; health record; insight; interest; interoperability; machine learning model; mild cognitive impairment; multiple chronic conditions; novel; outcome prediction; patient stratification; patient subsets; pharmacologic; phenotyping algorithm; racial population; recruit; response; screening; structured data; success; tool; unstructured data

Summary The fragmented clinical data in EHRs and trials makes it hard to study the relationship between Alzheimer's disease (AD) and multiple chronic diseases (MCC). This is because the data is often spread out across different platforms and databases, making it difficult to get a complete picture. In addition, the data is often incomplete. This can lead to gaps in research and missed opportunities to understand MCC’s contribution to AD progression. To overcome these challenges, we will develop interoperable electronic health records (EHR) with an application programming interface (API) that follows the standard data format, i.e., Fast Healthcare Interoperability Resources (FHIR). Partnering with ACTIVE MIND, an interventional trial that examines the potential efficacy of cognitive training (CT) in reducing dementia incidences, we will link, consent, extract and harmonize local EHRs and other relevant health information from ~1,000 patients. We will develop ontology models and use them to guide the natural language processing (NLP) models to distill, organize, and convert MCC and relevant concepts into FHIR-accessible data. Using these data together with FHIR-mapped structured data, we propose a demonstration project to develop novel missing data imputation and computational phenotyping models to stratify heterogeneous subpopulations based on longitudinal MCC patterns to predict their AD onset risks.

总结 电子健康记录和试验中支离破碎的临床数据使得研究阿尔茨海默氏症与 疾病（AD）和多种慢性疾病（MCC）。这是因为数据通常分布在不同的 平台和数据库，很难得到一个完整的图片。此外，数据往往不完整。 这可能导致研究空白，并错过了解MCC对AD进展的贡献的机会。 为了克服这些挑战，我们将开发具有应用程序的可互操作电子健康记录（EHR 遵循标准数据格式的编程接口（API），即，快速医疗保健互操作性 资源（FHIR）。与ACTIVE MIND合作，这是一项干预性试验，旨在检查 认知训练（CT）在降低痴呆症发病率，我们将链接，同意，提取和协调当地的EHR 以及约1,000名患者的其他相关健康信息。我们将开发本体模型，并使用它们来 引导自然语言处理（NLP）模型提取、组织和转换MCC及相关概念 转换成FHIR可访问的数据。使用这些数据与FHIR映射的结构化数据，我们提出了一个 示范项目，开发新的缺失数据填补和计算表型模型， 基于纵向MCC模式的异质亚群来预测其AD发病风险。