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）： 每位患者平均约1,000美元，招聘仍然是人类研究的昂贵瓶颈。电子健康记录（EHR）的迅速采用使电子预筛选（以下文章）成为对这种瓶颈的可行解决方案。我们的长期目标是实现这一“圣杯”。我们这种竞争延续的短期目标是开发聪明的患者查询顾问，以提高电子筛查的准确性和效率。 电子筛查的困难之一是资格标准和 2临床数据。2每个资格标准（例如高血压）描述了患者的特征， 这与EHR中的多种数据特征（例如，高血压药物的命令，血压升高和高血压症状）相关。此外，每个数据功能可能具有不同数据源的多个语义表示（例如“ SBP”，“ BP”或“血压”）。例如，可以在急诊室，医生办公室，ICU和住院单元中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须表明长期高血压。 使用临床数据来识别有资格获得临床研究的患者需要专业知识和专家指导，以导航数据特征的庞大空间以及从数据特征中从数据特征中推断出来以确定资格确定。用户必须了解 在使用可用数据的特征之前，它们可以搜索患者。例如，当只有5％的高血压患者具有高血压的ICD-9代码，但其中73％的患者具有高血压药物订单时，使用药物信息来构建高血压患者的查询将比使用ICD-9代码更有效。即使是复杂的生物医学数据查询工具，例如I2B2，VIEAGE和STINDER也只被动地将用户指定的数据功能转化为查询语句。他们没有指导研究人员选择数据功能及其最合适的语义表示或数据源。几乎没有援助可以为研究人员提供数据特征或帮助他们进行探索性数据分析以进行最佳数据特征选择。 混合发射互动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，可能会满足这一需求。我们假设，通过为生物医学研究人员提供基于知识的混合启动性对话系统，我们可以通过支持相关数据特征的探索性分析来最大程度地提高电子筛选的效率和准确性，以进行查询优化。我们的方法具有创新性，因为它（1）满足了用户对临床数据的智能查询接口的需求，（2）为基于相关数据功能提供了一种新颖的数据驱动方法来确定资格确定，并且（3）通过支持人类计算机的协作解决问题来启用有效的查询优化。 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赞助的临床试验。7,13,21我们开发了一种结合了PubMed知识和EHR数据的方法，以推断患者表型4并调和结构化和非结构化的临床数据，以支持E-Screening。18我们是通过方法和对基本块的初步理解来准备的，以最佳的理解，以最佳的方式将有资格的标准转化为数据特征；因此，我们当前的建议是逻辑下一步。 我们的具体目的是： 1。使用混合方法来了解生物医学研究人员的需求，以查询澄清，并确定查询分析师使用的共同策略以优化复杂资格查询。 2。开发一种基于知识的混合定位对话系统，以改善使用参与式设计方法来改善查询配方的人类计算机协作。 3。使用研究数据仓库和两种用例来评估混合启动对话系统的功效和可用性：研究协议可行性测试和试用招募预筛选。 我们将通过对生物医学研究人员之间的查询支持需求以及一种有效的电子屏幕化方法的了解来提高该领域，该方法结合了智能查询建议和Review 22的迭代查询，以通过人类计算机协作来改善研究人员的数据访问。