III: Medium: Collaborative Research: Data Mining and Cleaning for Medical Data Warehouses

III：媒介：协作研究：医疗数据仓库的数据挖掘和清理

• 批准号: 0964526
• 负责人: Christopher Jermaine
• 金额: $ 60万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964526&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Data

A clinical data warehouse (CDW) is a repository that aggregates medical patient data from many different sources: billing records, electronic medical records including structured data (e.g., codes for diagnoses, procedures, vital signs, etc.), semi-structured reports and free-text dictations. A key benefit of maintaining a CDW lies in its ability to provide the raw data that are needed for large-scale study of real-world health care -- for example, finding a previously unknown association between a pain killer (e.g., Vioxx) and heart disease. Unfortunately, CDWs are riddled with systematic errors that make it difficult to answer even the simplest questions (such as "What fraction of female outpatients have breast cancer?") with any accuracy.This project focuses on statistical models and learning algorithms for quantifying and correcting errors in CDW records. For example, the project is developing semi-supervised learning methods that use the structured data present in electronic medical records (patient age, weight, medications, billing codes, etc.) in order to quantify the likelihood of error that is associated with the diagnosis codes present in the record (for example, being able to state "There is a 0.2 probability that the correct code was migraine instead of the listed headache"). The project will also develop methods that attempt to control for confounding variables present in the records, in order to remove systematic biases from the data.These models and learning algorithms will allow CDW users to manage and monitor the uncertainty and error in the data. This in turn will allow fundamentally new types of analysis to be undertaken, which will result in the discovery of actionable medical knowledge that saves both lives and money. To make the models and algorithms accessible to medical professionals who may lack computational or statistical background, they will be added to an open-source release of the widely-used I2B2 CDW software.The project is a collaboration between the Computer Science Department at Rice University and the School of Biomedical informatics at the University of Texas Health Science Center at Houston. All project results will be made available online (http://www.cs.rice.edu/~cmj4/CDW.htm).

临床数据仓库 (CDW) 是一个聚合来自许多不同来源的医疗患者数据的存储库：计费记录、电子病历，包括结构化数据（例如诊断、程序、生命体征等代码）、半结构化报告和自由文本听写。维持 CDW 的一个关键好处在于它能够提供大规模现实世界医疗保健研究所需的原始数据，例如，发现止痛药（例如 Vioxx）和药物之间以前未知的关联。心脏病。不幸的是，CDW 充满了系统性错误，以至于很难准确地回答最简单的问题（例如“女性门诊患者中有多少比例患有乳腺癌？”）。该项目重点关注用于量化和纠正的统计模型和学习算法CDW 记录中的错误。例如，该项目正在开发半监督学习方法，该方法使用电子病历中的结构化数据（患者年龄、体重、药物、账单代码等）来量化与诊断相关的错误可能性记录中存在的代码（例如，能够声明“正确代码是偏头痛而不是列出的头痛的概率为 0.2”）。该项目还将开发尝试控制记录中存在的混杂变量的方法，以消除数据中的系统偏差。这些模型和学习算法将允许 CDW 用户管理和监控数据中的不确定性和错误。 这反过来又将允许进行全新类型的分析，从而发现可操作的医学知识，从而挽救生命和金钱。 为了使缺乏计算或统计背景的医疗专业人员能够使用这些模型和算法，它们将被添加到广泛使用的 I2B2 CDW 软件的开源版本中。该项目是莱斯大学计算机科学系之间的合作项目以及休斯顿德克萨斯大学健康科学中心的生物医学信息学院。 所有项目结果将在线提供（http://www.cs.rice.edu/~cmj4/CDW.htm）。