Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data

弥合研究资格标准和临床数据之间的语义差距

• 批准号: 8884643
• 负责人: CHUNHUA WENG
• 金额: $ 34.58万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2017-07-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8884643
• 关键词: Accident and Emergency department; Address; Adoption; Blood Pressure; Characteristics; Chronic; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Complex; Computers; Data; Data Analyses; Data Sources; Databases; Drug Formulations; Drug usage; Electronic Health Record; Electronics; Eligibility Determination; Funding; Goals; Human; Hypertension; ICD-9; Knowledge; Methods; Participant; Patients; Pharmaceutical Preparations; Problem Solving; Protocols documentation; PubMed; Reading; Recommendation; Research; Research Personnel; Scientist; Semantics; Solutions; Specific qualifier value; Structure; Symptoms; System; Testing; Text; Translating; Translational Research; United States National Institutes of Health; base; cost; data space; design; experience; improved; information organization; innovation; knowledge base; novel; query optimization; screening; tool; usability

DESCRIPTION (provided by applicant): Averaging about $1,000 per patient1, recruitment remains an expensive bottleneck for human studies. The rapidly increasing adoption of electronic health records (EHR) has made electronic prescreening (E-screening hereafter) a practicable solution to this bottleneck. Our long-term goal is to achieve this "holy grail". Our short- term goal of this competing continuation is to develop an intelligent patient query consultant to improve the accuracy and efficiency of E-screening. One of the difficulties for E-screening is the semantic gap between eligibility criteria and clinical data.2 Each eligibility criterion (e.g., hypertension) describes a patient characteristic, which is correlated with multiple data features (e.g., orders of hypertension drugs, elevated blood pressure, and symptoms of hypertension) in EHR. Moreover, each data feature may have multiple semantic representations (e.g., "SBP", "BP", or "blood pressure") from disparate data sources. For example, elevated systolic blood pressure can be recorded in varying formats in an emergency room, a doctor's office, an ICU, and an in-patient unit, but not all of these readings necessarily indicate chronic hypertension. The use of clinical data to identify patients eligible for clinical research requires specialized knowledge and expert guidance to navigate the vast space of data features and intelligent inferences from data features for eligibility determination. A user must understand the characteristics of available data before using them to search for patients. For example, when only 5% of hypertensive patients have ICD-9 codes for hypertension but 73% of these patients have hypertension drug orders, using drug information to construct a query of hypertensive patients will be more effective than one using ICD-9 codes. Even sophisticated biomedical data query tools such as i2b2, VISAGE, and STRIDE only passively translate user-specified data features into a query statement. They do not guide a researcher in selecting a data feature and its most appropriate semantic representations or data sources. Little aid is available to inform researchers about data characteristics or to help them conduct exploratory data analyses for optimal data feature selection. Mixed-initiative interaction,3 which allows human and computer to collaboratively contribute to converged problem solutions, can potentially fulfill this need. We hypothesize that by equipping biomedical researchers with a knowledge-based, mixed-initiative dialog system, we can maximize the efficiency and accuracy of E- screening by supporting exploratory analyses of correlated data features for query optimization. Our approach is innovative because it (1) addresses the user needs for intelligent query interfaces for clinical data, (2) provides a novel data-driven approach to eligibility determination based on correlated data features, and (3) enables efficient query optimization through support of human-computer collaborative problem solving. We will build on the results from our first funding period for bridging the semantic gap.4-21 We developed an analysis pipeline called EliXR to construct a semantic knowledge representation for eligibility criteria 6,9,16,17, which can be used to transform free-text eligibility criteria ito structured narrative.6 We developed methods to dynamically categorize eligibility criteria by data type.8 We accumulated E-screening experience from three NIH-sponsored clinical trials.7,13,21 We developed a method combining PubMed knowledge and EHR data to infer patient phenotype4 and reconciled structured and unstructured clinical data to support E-screening.18 We are prepared with methods and a preliminary understanding of the building blocks necessary to optimally translate eligibility criteria into data features; therefore, our current proposal is the logical next step. Our specific aims are to: 1. Use mixed methods to understand the needs of biomedical researchers for query clarification and identify common strategies used by query analysts for plan optimization for complex eligibility queries. 2. Develop a knowledge-based, mixed-initiative dialog system to improve human-computer collaboration for query formulation using participatory design methods. 3. Evaluate the efficacy and usability of the mixed-initiative dialog system using a research data warehouse and two use cases: research protocol feasibility testing and trial recruitment prescreening. We will advance the field by contributing knowledge of the needs for query support among biomedical researchers and an effective E-screening method that combines intelligent query recommendation and iterative query by review22 to improve data access for researchers through human-computer collaboration.

描述（由申请人提供）： 招募每位患者的平均费用约为 1,000 美元1，招募仍然是人体研究的一个昂贵的瓶颈。电子健康记录 (EHR) 的迅速普及使得电子预筛查（以下简称电子筛查）成为解决这一瓶颈的切实可行的解决方案。我们的长期目标是实现这个“圣杯”。我们这一竞争性延续项目的短期目标是开发智能患者查询顾问，以提高电子筛查的准确性和效率。 电子筛选的困难之一是资格标准和标准之间的语义差距。 临床数据。2 每个资格标准（例如高血压）描述了患者特征， 这与 EHR 中的多个数据特征（例如高血压药物的使用顺序、血压升高和高血压症状）相关。此外，每个数据特征可能具有来自不同数据源的多种语义表示（例如，“SBP”、“BP”或“血压”）。例如，在急诊室、医生办公室、重症监护病房和住院病房可以以不同的格式记录升高的收缩压，但并非所有这些读数都必然表明慢性高血压。 使用临床数据来识别符合临床研究资格的患者需要专业知识和专家指导，以驾驭广阔的数据特征空间，并从数据特征中进行智能推断，从而确定资格。用户必须了解 在使用可用数据搜索患者之前先了解其特征。例如，当只有 5% 的高血压患者有高血压的 ICD-9 代码，但其中 73% 的患者有高血压药物订单时，使用药物信息构建高血压患者查询将比使用 ICD-9 代码更有效。即使是复杂的生物医学数据查询工具，如 i2b2、VISAGE 和 STRIDE，也只是被动地将用户指定的数据特征转换为查询语句。它们不指导研究人员选择数据特征及其最合适的语义表示或数据源。几乎没有任何帮助可以让研究人员了解数据特征或帮助他们进行探索性数据分析以选择最佳数据特征。 混合主动交互3允许人类和计算机协作为融合的问题解决方案做出贡献，有可能满足这一需求。我们假设，通过为生物医学研究人员配备基于知识的混合主动对话系统，我们可以通过支持对相关数据特征进行探索性分析以进行查询优化，从而最大限度地提高电子筛选的效率和准确性。我们的方法是创新的，因为它（1）满足了用户对临床数据智能查询界面的需求，（2）提供了一种新颖的数据驱动方法来基于相关数据特征来确定资格，（3）通过支持实现高效的查询优化人机协作解决问题的方法。 我们将在第一个资助期的成果基础上缩小语义差距。4-21 我们开发了一个名为 EliXR 的分析管道，为资格标准 6、9、16、17 构建语义知识表示，可用于转换自由文本资格标准和结构化叙述。6 我们开发了按数据类型动态分类资格标准的方法。8 我们从 NIH 资助的三项临床试验中积累了电子筛选经验。7,13,21 我们开发了结合 PubMed 知识和 EHR 数据来推断患者表型4 并协调结构化和非结构化临床数据以支持电子筛查的方法。18 我们已准备好方法和对将资格标准最佳地转化为数据特征所需的构建模块的初步了解；因此，我们当前的提议是合乎逻辑的下一步。 我们的具体目标是： 1. 使用混合方法来了解生物医学研究人员对查询澄清的需求，并确定查询分析师用于复杂资格查询的计划优化的常用策略。 2. 开发基于知识的混合主动对话系统，以使用参与式设计方法改进查询制定的人机协作。 3. 使用研究数据仓库和两个用例评估混合主动对话系统的有效性和可用性：研究方案可行性测试和试验招募预筛选。 我们将通过贡献有关生物医学研究人员查询支持需求的知识以及将智能查询推荐和审查迭代查询相结合的有效电子筛选方法来推动该领域的发展，从而通过人机协作改善研究人员的数据访问。