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

临床数据仓库(CDW)是从许多不同来源聚合医疗患者数据的储存库：账单记录、包括结构化数据(例如，用于诊断、程序、生命体征等的代码)的电子医疗记录、半结构化报告和自由文本口述。维持CDW的一个关键好处在于它能够提供对现实世界医疗保健进行大规模研究所需的原始数据--例如，发现止痛药(如万络)和心脏病之间以前未知的联系。不幸的是，CDW充斥着系统性错误，这使得即使是最简单的问题也很难回答(例如：女性门诊患者中有多少患有乳腺癌？)这个项目的重点是量化和纠正CDW记录中的错误的统计模型和学习算法。例如，该项目正在开发半监督学习方法，使用电子病历中存在的结构化数据(患者年龄、体重、药物、账单代码等)。为了量化与记录中存在的诊断代码相关联的错误的可能性(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”)。该项目还将开发试图控制记录中存在的混杂变量的方法，以消除数据中的系统性偏差。这些模型和学习算法将使CDW用户能够管理和监控数据中的不确定性和错误。这反过来将允许从根本上进行新类型的分析，这将导致发现可操作的医学知识，既可以拯救生命，也可以拯救金钱。为了让可能缺乏计算或统计学背景的医学专业人员能够访问这些模型和算法，它们将被添加到广泛使用的I2B2 CDW软件的开源版本中。该项目是赖斯大学计算机科学系和德克萨斯大学休斯顿健康科学中心生物医学信息学学院的合作项目。所有项目结果都将在线提供(http://www.cs.rice.edu/~cmj4/CDW.htm).

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).