Concept-Level Methods for Comparative Effectiveness Research

描述（由申请人提供）：挑战领域和特定挑战主题：本申请涉及广泛的挑战领域（10）处理医疗保健数据的信息技术和特定挑战主题，10- rr -101*：促进医疗保健数据用于研究的二次使用的信息技术示范项目。临床数据仓库（cdw）归档来自电子医疗记录（emr）的数据。与电子病历不同，电子病历旨在存储和检索患者的数据（例如，关于约翰·史密斯的所有数据），cdw支持跨患者查询（例如，服用阿司匹林和不服用阿司匹林的患者发展为不稳定心绞痛的百分比）。cdw是基础设施的关键组件，可以重用医疗保健数据进行研究。因此，它们是比较有效性研究（CER）的重要推动者。然而，仅仅将医疗保健数据从emr传输到CDW是不够的。与临床试验数据不同，医疗保健数据的收集没有考虑到研究问题。因此，它们可能结构不良（例如，诊断的自由文本列表，而不是ICD9术语列表），并且包含受保护的健康信息（例如，姓名、地址）或识别短语，如“患有淋巴瘤的参议员”。我们的统一假设是，概念级方法可以应用于cdw，在保护主体隐私的同时为大量医疗保健数据带来意义。为了验证这一假设，我们将：1)调整和评估我们的新索引系统（基于图分析，b谷歌的PageRank算法的修改）以改进临床文本的概念提取，2)通过在概念级别处理临床文本来评估为“受试者”提供的隐私，3)调整和评估现有的可视化技术以可视化概念级医疗数据之间的关系，从而促进生物医学研究人员的探索性数据分析。虽然这些目标是相互建立的，但即使其他目标失败，每个目标也能成功。在本项目结束时，我们将开发和评估使用医疗保健数据的CER的新概念提取算法。我们将确定处理概念级数据的隐私含义，并开发用于大型医疗保健数据集的概念级浏览的交互式可视化。许多组织正在建立临床数据仓库，以便进行比较有效性研究。然而，仅仅将电子医疗记录中的数据加载到临床数据仓库是不够的。为了重用医疗保健数据用于研究，我们将开发新的方法来访问数据仓库中的临床数据并使其可视化。具体来说，我们将开发新的方法来从非结构化文本中提取概念，可视化大型数据集以快速查看模式并确定我们的方法对隐私的影响。

DESCRIPTION (provided by applicant): Challenge Area and Specific Challenge Topic: This application addresses broad Challenge Area (10) Information Technology for Processing Health Care Data and specific Challenge Topic, 10-RR-101*: Information Technology Demonstration Projects Facilitating Secondary Use of Healthcare Data for Research. Clinical Data Warehouses (CDWs) archive data from electronic medical records (EMRs). Unlike EMRs, which are designed to store and retrieve data by patient (e.g., all data about John Smith), CDWs support queries across patients (e.g., percentage of patients on vs. off aspirin who develop unstable angina). CDWs are critical components of an infrastructure that enables reuse of healthcare data for research. As such, they are important enablers of comparative effectiveness research (CER). However, simply transferring healthcare data from EMRs to a CDW is not sufficient. Healthcare data, unlike clinical trial data, are not collected with a research question in mind. Thus, they may be poorly structured (e.g., free-text list of diagnoses, not a list of ICD9 terms) and contain protected health information (e.g., names, addresses) or identifying phrases such as "senator with lymphoma." Our unifying hypothesis is that concept-level approaches can be applied to CDWs to bring meaning to vast amounts of healthcare data while protecting subject privacy. To test this hypothesis, we will: 1) adapt and evaluate our novel indexing system (based on graph analysis, a modification of Google's PageRank algorithm) to improve concept extraction from clinical text, 2) evaluate the privacy afforded to "subjects" by working with clinical text at the concept level and 3) adapt and evaluate existing visualization techniques to visualize relationships among concept-level healthcare data, thereby facilitating exploratory data analysis by biomedical researchers. Although these aims build on each other, every individual aim can succeed even if the others fail. At the conclusion of this project we will have developed and evaluated novel concept-extraction algorithms for CER using healthcare data. We will have determined the privacy implications of working with concept-level data and developed interactive visualizations for concept-level browsing of large healthcare data sets. Many organizations are building clinical data warehouses to enable comparative effectiveness research. However, simply loading data from electronic medical records into clinical data warehouses is not enough. To enable reuse of healthcare data for research, we will develop new ways to access and visualize clinical data within data warehouses. Specifically, we will develop new ways to extract concepts from unstructured text, visualize large data sets to quickly see patterns and determine the privacy implications of our methods.