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.

项目摘要为了回应（Par-18-896），该提案的总体目标是完全发展统计学家，医学信息家，临床医生之间的共同努力，重点是发展通过现代知识工程和数据驱动的严格偏见校正框架统计建模，用于改善卫生系统数据的无偏见和可重复性驱动的研究。在此提案中，我们将重点介绍：（1）开发一种新颖的先验知识指导的综合通过纳入先前的表型精度来实现偏差校正的可能性方法。（2）开发方法和算法以说明结果和结果中的EHR表型错误预测指标。（3）验证，应用和软件开发。我们将使用拟议的偏向校正方法与几个EHR数据集复制现有发现并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。我们还将为提出的方法开发软件，以促进基于EHR的临床研究。