PheBC: bias correction methods for EHR derived phenotype

PheBC：EHR 衍生表型的偏差校正方法

基本信息

批准号：
10471166
负责人：
Yong Chen
金额：
$ 34.2万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10471166
关键词：
Adverse event Algorithms Cations Chronic Clinical Clinical Investigator Clinical Research Computer software Data Data Science Data Set Electronic Health Record Engineering Evaluation Failure Feasibility Studies Goals Health Health system Individual Informatics Information Retrieval Investigation Joints Knowledge Knowledge Discovery Lead Measurement Medical Methodology Methods Modeling Modernization Non-Insulin-Dependent Diabetes Mellitus Outcome Paper Patients Pennsylvania Phenotype Pilot Projects Population Procedures Publishing Reproducibility Reproducibility of Results Research Research Personnel Risk Factors Sampling Studies Statistical Models System Texas Translational Research Universities Validation base cohort health data high standard improved multiple datasets novel outcome prediction phenotyping algorithm response software development tool

项目摘要

Project Summary In response to the (PAR-18-896), the overarching goal of this proposal is to fully develop a joint effort between statisticians, medical informaticians, clinicians with a focus on developing a rigorous bias correction framework through modern knowledge engineering and data-driven statistical modeling, for improving the unbiasedness and reproducibility of health system data driven research. In this proposal, we will focus on: (1) Develop a novel prior-knowledge-guided integrated likelihood approach to enable bias correction by incorporating prior phenotyping accuracy. (2) Develop methods and algorithms to account for EHR phenotyping errors in both outcomes and predictors. And (3) Validation, Application and Software development. We will use the proposed bias correction methods to several EHR datasets to replicate existing findings and investigate new hypothesis in multiple datasets at University of Texas and University of Pennsylvania. We will also develop software for the proposed methods to facilitate ongoing EHR-based clinical studies.

项目概要为了响应 (PAR-18-896)，该提案的总体目标是充分开发统计学家、医学信息学家、临床医生的共同努力，重点是开发通过现代知识工程和数据驱动建立严格的偏差纠正框架统计模型，用于提高卫生系统数据的公正性和可重复性驱动的研究。在本提案中，我们将重点关注：（1）开发一种新颖的先验知识引导的集成似然方法通过结合先前的表型准确度来实现偏差校正。 (2) 开发方法和算法来解释 EHR 表型分析结果和结果中的错误预测因子。 (3) 验证、应用和软件开发。我们将使用建议的对多个 EHR 数据集的偏差校正方法，以复制现有发现并进行调查德克萨斯大学和宾夕法尼亚大学的多个数据集中的新假设。我们还将为所提出的方法开发软件，以促进正在进行的基于 EHR 的临床研究。