Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data

描述（由申请人提供）： 平均每位患者约1，000美元1，招募仍然是人类研究的昂贵瓶颈。电子健康记录（EHR）的快速增长使电子预筛选（以下简称E-screening）成为解决这一瓶颈的可行方案。我们的长期目标是实现这一“圣杯”。我们的短期目标是开发一个智能的病人查询顾问，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 临床数据。2每个合格标准（例如，高血压）描述了患者特征， 其与多个数据特征相关（例如，高血压药物的顺序、血压升高和高血压的症状）。此外，每个数据特征可以具有多个语义表示（例如，“SBP”、“BP”或“血压”）。例如，在急诊室、医生办公室、ICU和住院部，可以以不同的格式记录收缩压升高，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条件的患者需要专业知识和专家指导，以导航数据特征的巨大空间和来自数据特征的智能推断，以确定合格性。用户必须了解 在使用可用数据搜索患者之前，先分析可用数据的特征。例如，当只有5%的高血压患者具有高血压的ICD-9代码，但这些患者中的73%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代码的查询更有效。即使是复杂的生物医学数据查询工具，如i2 b2、VISAGE和STRIDE，也只能被动地将用户指定的数据特征转换为查询语句。它们不能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。很少有援助，以告知研究人员的数据特征，或帮助他们进行探索性的数据分析，以最佳的数据特征选择。 混合主动交互，3允许人类和计算机协作地为融合的问题解决方案做出贡献，可以潜在地满足这一需求。我们假设，通过为生物医学研究人员配备一个基于知识的混合主动对话系统，我们可以通过支持相关数据特征的探索性分析来优化查询，从而最大限度地提高E-筛选的效率和准确性。我们的方法是创新的，因为它（1）解决了用户对临床数据智能查询界面的需求，（2）提供了一种基于相关数据特征的新型数据驱动的资格确定方法，以及（3）通过支持人机协作解决问题来实现高效的查询优化。 我们将以第一个资助期的结果为基础，弥合语义差距。4 -21我们开发了一个名为EliXR的分析管道，以构建资格标准6，9，16，17的语义知识表示，它可以用来将自由文本的资格标准转换为结构化的叙述。6我们开发了按数据类型动态分类资格标准的方法。8我们积累了E-从三个NIH赞助的临床试验中获得的筛选经验。7，13，21我们开发了一种结合PubMed知识和EHR数据的方法，以推断患者表型4并协调结构化和非结构化临床数据，以支持电子筛选。18我们准备了方法，并初步了解了将资格标准最佳转化为数据特征所需的构建模块;因此，我们目前的建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见策略。 2. 开发一个基于知识的，混合主动的对话系统，以提高人机协作查询公式使用参与式设计方法。 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。 我们将通过贡献生物医学研究人员查询支持需求的知识和有效的电子筛选方法来推进该领域，该方法将智能查询推荐和迭代查询结合起来，通过人机协作来改善研究人员的数据访问。

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.