Using Digital Signals from Credit Data for Early Detection of Alzheimer's Disease and Related Dementias

使用信用数据中的数字信号早期检测阿尔茨海默病和相关痴呆症

基本信息

批准号：
10590416
负责人：
CAROLE R GRESENZ
金额：
$ 69.14万
依托单位：
GEORGETOWN 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：
2023-02-01 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10590416
关键词：
Acceleration Address Adult Affect Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Automobile Driving Biological Markers Brain imaging Caring Characteristics Computational algorithm Data Data Sources Databases Dementia Diagnosis Disease Early Diagnosis Early identification Economics Education Enrollment Equilibrium Equity Ethnic Origin Family Gender Goals Habits Health Home Household Individual Life Style Liquid substance Machine Learning Medicare Medicare claim Monitor Moods Neurobehavioral Manifestations New York Outcome Outcome Measure Patients Persons Pharmaceutical Preparations Predisposition Probability Quality of life Race Research Risk Risk Factors Safety Screening procedure Signal Transduction Signs and Symptoms Social Security Number Source Structure Support System Symptoms Time Update comorbidity cost diagnostic tool digital digital technology emerging adult experience financial decision making machine learning algorithm machine learning method machine learning model modifiable risk payment prediction algorithm preference research clinical testing response tool

项目摘要

Project Summary The value of early diagnosis for Alzheimer’s disease and related dementias (ADRD) is increasingly recognized. However, available diagnostic tools rely primarily on the manifestation of cognitive symptoms that interfere with everyday activities, and screening tools to support earlier identification of individuals with ADRD are lacking. Credit data represent a unique foundational data source upon which machine learning algorithms can be developed to identify individuals at risk for ADRD and facilitate earlier diagnosis. The strength of the information signal from credit data for identifying those at risk for ADRD is supported by previous research that finds, first, that significant limitations and rapid declines in financial capacity are a hallmark of early-stage disease and, second, that afflicted individuals and their families experience negative economic consequences during early-stage disease. We propose using a massive database—that we have already constructed—of credit data from Equifax which is the basis of the Federal Reserve Bank of New York’s Consumer Credit Panel (CCP), merged at the individual level using a unique common identifier (Social Security number), with Medicare enrollment and claims data. The data encompass more than 84 million person-years of data in total, with more than 1.7 million individuals who have been diagnosed with ADRD. Our specific aims are to: (1) Estimate the effects of early-stage ADRD on a wide range of financial outcomes measured in credit data, allowing for potential differences in the effects of early-stage ADRD depending on characteristics such as race/ethnicity, education, gender, and household structure; (2) Apply machine learning methods to our already- developed massive data base with merged credit (CCP) and Medicare data in order to develop algorithms that are capable of identifying individuals at risk for ADRD; and (3) Assess the robustness of the algorithm to the inclusion of newly available years of Medicare claims and enrollment data. The findings from Specific Aim 1 are important for identifying and understanding the specific financial outcomes individuals with ADRD are most susceptible to during the early stage of disease and will help inform the machine learning models in Specific Aims 2 and 3.

项目概要阿尔茨海默氏病和相关痴呆症 (ADRD) 的早期诊断价值日益凸显然而，现有的诊断工具主要依赖于认知症状的表现。干扰日常活动，以及支持早期识别 ADRD 患者的筛查工具信用数据是机器学习算法所依赖的独特的基础数据源。可以开发用于识别有 ADRD 风险的个体并促进早期诊断。先前的研究支持来自信用数据的信息信号，用于识别 ADRD 风险人群首先，财务能力的严重限制和迅速下降是早期阶段的一个标志。其次，受影响的个人及其家庭会遭受负面的经济后果我们建议使用我们已经构建的庞大数据库。来自 Equifax 的信贷数据，这是纽约联邦储备银行消费者信贷小组的基础 (CCP)，使用唯一的通用标识符（社会安全号码）在个人层面进行合并，该数据总共包含超过 8400 万人年的数据，超过 170 万人被诊断患有 ADRD，我们的具体目标是：(1) 估计早期 ADRD 对信用数据衡量的各种财务结果的影响，考虑到早期 ADRD 的影响存在潜在差异，具体取决于以下特征：种族/民族、教育、性别和家庭结构；（2）将机器学习方法应用于我们已经- 开发了包含合并信用 (CCP) 和医疗保险数据的海量数据库，以便开发算法能够识别有 ADRD 风险的个人；以及 (3) 评估算法对 ADRD 的稳健性；纳入新获得的 Medicare 索赔和登记数据年份。特定目标 1 的调查结果。对于识别和理解 ADRD 患者最常遭受的具体财务结果非常重要在疾病的早期阶段容易受到影响，并将有助于为特定的机器学习模型提供信息目标 2 和 3。