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

临床数据仓库（CDW）是汇总来自许多不同来源的医疗患者数据的存储库：账单记录、包括结构化数据（例如诊断代码、程序、生命体征等）的电子医疗记录、半结构化报告和自由文本听写。维护CDW的一个关键好处在于，它能够提供对现实世界卫生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）与心脏病之间以前未知的关联。不幸的是，cdw充满了系统性的错误，使得即使是最简单的问题（比如“女性门诊患者中有多少比例患有乳腺癌？”）也很难回答。准确地说。这个项目的重点是统计模型和学习算法，用于量化和纠正CDW记录中的错误。例如，该项目正在开发半监督学习方法，该方法使用电子医疗记录中的结构化数据（患者年龄、体重、药物、账单代码等）来量化与记录中存在的诊断代码相关的错误可能性（例如，能够声明“有0.2的概率正确的代码是偏头痛，而不是列出的头痛”）。该项目还将开发一些方法，试图控制记录中存在的混淆变量，以消除数据中的系统性偏差。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基本的新型分析，这将导致发现可操作的医学知识，从而挽救生命和金钱。为了使可能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和休斯顿德克萨斯大学健康科学中心生物医学信息学院的合作项目。所有项目结果将在网上公布（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).