SMILE-PD: Similarity Matching In Longitudinal Electronic Patient Data

SMILE-PD：纵向电子患者数据中的相似性匹配

基本信息

批准号：
10799090
负责人：
MARK G WEINER
金额：
$ 19.07万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
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：
2023-09-06 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10799090
关键词：
Affinity Algorithms All of Us Research Program Caring Cells Characteristics Chemical Structure Chronic Disease Clinical Clinical Data Comparative Effectiveness Research Computational algorithm Computerized Medical Record Consumption DNA Data Data Set Data Sources Diagnosis Disease Disease Outcome Electronics Environmental Risk Factor Etiology Evaluation Generations Genetic Genomics Goals Healthcare Heart failure Hospitalization Hypothyroidism Individual Information Resources Investigation Learning Link Location Manuals Measures Medical Mental Depression Mental disorders Modeling Non-Insulin-Dependent Diabetes Mellitus Observational Study Outcome Patient-Focused Outcomes Patients Peripheral arterial disease Pharmaceutical Preparations Phenotype Population Predictive Analytics Predisposition Prevention strategy Process Public Health Informatics Records Reproducibility Research Research Personnel Resources Risk Risk Assessment Smiling Specific qualifier value Time United States National Institutes of Health Visualization Work biobank cohort comorbidity demographics effectiveness evaluation endophenotype genome wide association study genomic data improved interest learning algorithm learning strategy lifestyle factors open source precision medicine side effect sound trait treatment strategy vector

项目摘要

PROJECT SUMMARY The NIH All of Us (AoU) Research Program is a national research initiative launched by the National Institutes of Health (NIH) in May 2018 with the goal of advancing precision medicine, which aims to provide personalized medical treatment and prevention strategies based on an individual's unique genetic, environmental, and lifestyle factors. Analysis of real world data like AoU typically requires creation of cohorts that are clinically similar with the exception of the characteristic(s) being evaluated. If the creation of compared cohorts is not conducted with care, results may be confounded by systematic difference in the characteristics of patients assigned to the compared cohorts. This proposal, Similarity Matching In Longitudinal Electronic Patient Data (SMILE PD), will apply a sophisticated patient matching algorithm within an easy- to-use interface to help Investigators to create suitable analytical cohorts to enable generation of accurate, reproducible real world evidence from the rich content of the All of Us data. The project has 4 Aims : • Aim 1. Develop algorithms that learn patient similarities from AoU data in a comprehensive manner. • Aim 2: Develop approaches that can integrate drug and disease information from publicly available data sources to improve the quality of learned patient similarities. • Aim 3: Implement an interactive user interface to specify similarity matching criteria and demonstrate the learned patient similarities • Aim 4 : Apply the patient similarity matching algorithm and interface to conduct an initial assessment of the impact of mental illness and its treatment on chronic disease outcomes The patient similarity matching interface created through this proposal will enable investigators to conduct predictive analytics, risk assessments and comparative effectiveness research in a more robust and reproducible fashion.

项目摘要 NIH我们所有人（AOU）研究计划是一项国家研究计划。美国国立卫生研究院（NIH）于2018年5月，目的是推进精密医学，旨在基于个人独特的遗传，环境和生活方式因素。分析现实世界数据通常需要创建在临床上相似的同类队列，除了评估特征。如果没有进行比较的队列的创建护理，结果可能会因患者特征的系统差异而混淆分配给比较的队列。该建议，纵向电子中的相似性匹配患者数据（Smile PD），将在易于 - 使用的接口，以帮助调查人员创建合适的分析队列以使生成来自我们所有数据的丰富内容的准确，可再现的现实世界证据。该项目有4个目标： •AIM 1。开发算法，从AOU数据中学习患者相似性全面的方式。 •AIM 2：开发可以将药物和疾病信息整合的方法可公开可用的数据源，以提高学识渊博的患者相似性的质量。 •AIM 3：实现交互式用户界面以指定相似性匹配条件并展示博学的患者相似性 •AIM 4：应用与匹配算法和界面相匹配的患者相似性进行执行对精神疾病及其对慢性疾病的治疗的影响的初步评估结果通过此建议创建的患者相似性匹配界面将使调查人员能够进行预测分析，风险评估和比较有效性研究强大而可重复的时尚。