Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data

项目摘要 我们的长期目标是利用信息学优化人类临床研究的设计和进行1。 资格标准定义了每个人类研究的研究人群。它们的清晰度、准确性和精确度是 这对参与者招募、结果传播和证据合成的成功至关重要。我们的目标是 更新是构建一个数据驱动的、基于知识的决策辅助工具，供现实生活中的临床研究人员进行优化 研究资格标准定义。 资格标准(例如，患有2型糖尿病)在语义表示上的差异 并且其作为临床变量的可操作性(例如，HbA1C≥6.5%或ICD-9代码=‘250.00’)已经被定义 作为语义GAP2，它的关闭对生物医学信息学来说是一个巨大的挑战2，3。我们的研究 有助于深入理解这一语义鸿沟以及它如何限制计算重用和效率 将资格标准传达给临床研究的关键利益相关者4-9。我们已经发展了信息学 通过将自由文本资格标准转换为半结构化格式来帮助弥补这一差距的方法，以帮助 研究队列确定10-13，相关临床试验的总体代表性分析14-19，正文 挖掘共同资格特征及其趋势18、20-24，并确定有问题的排除标准 用于精神障碍试验25。我们使用其中的几种方法开发了一个名为VITTA17的可视化系统 这表明临床试验人群的资格标准和临床特征在相关试验中是如何变化的。 更重要的是，我们的研究揭示了语义鸿沟的一个未被充分研究的根本原因，即 资格标准通常定义不明确、不准确、不具体或不精确，并且不容易翻译为 标准必须操作的真实世界电子健康记录(EHR)数据表示。这个 大患者数据的出现为利用现实世界的特征提供了前所未有的机会 患者指导和告知数据驱动的资格标准的精确定义25。通过定义特征 在预期研究人群中，资格标准严重影响一个国家的人口代表性 临床研究，进一步影响患者安全和研究结果的可重复性和可重复性之间的权衡 概括性。我们假设，通过将患者数据，包括临床和基因组数据，与公众 临床试验信息，我们可以主动指导研究人员优化精确度、招募可行性 以及资格标准的代表性。这项研究将展示一种新颖的数据驱动和 以知识为基础的系统，通过创新的信息学，帮助研究人员优化资格标准 用于集成专有和公共数据以进行深度表型分析、目标人群概况分析和 人口代表性的量化和可视化。

Project Summary Our long-term goal is to optimize the design and conduct of human clinical research using informatics1. Eligibility criteria define the study population for every human study. Their clarity, accuracy and precision are crucial to the success of participant recruitment, results dissemination, and evidence synthesis. Our goal for this renewal is to build a data-driven and knowledge-based decision aid for real-life clinical researchers to optimize research eligibility criteria definition. The difference in the semantic representation of an eligibility criterion (e.g., having Type 2 diabetes mellitus) and its operationalization as a clinical variable (e.g., HbA1C ≥ 6.5% or ICD-9 code = ‘250.00’) has been defined as the semantic gap2, the closing of which is a grand challenge for biomedical informatics2,3. Our research has contributed to the in-depth understanding of this semantic gap and how it limits computational reuse and effective communication of eligibility criteria to key stakeholders of clinical research4-9. We have developed informatics methods to help bridge this gap, by transforming free-text eligibility criteria into semi-structured formats to aid in study cohort identification10-13, analysis of the population representativeness of related clinical trials14-19, text mining of common eligibility features and their trends18,20-24, and identification of questionable exclusion criteria for mental disorder trials25. We used several of these methods to develop a visualization system called VITTA17 that shows how eligibility criteria and the clinical features of clinical trial populations vary across related trials. More importantly, our research has revealed an understudied root cause of the semantic gap, which is that eligibility criteria are often poorly defined, inaccurate, nonspecific, or imprecise, and not easily translatable to the real-world electronic health record (EHR) data representations to which the criteria must be operationalized. The advent of Big Patient Data offers an unprecedented opportunity to draw on the characteristics of real-world patients to guide and inform the data-driven precise definition of eligibility criteria25. By defining the characteristics of the intended study population, eligibility criteria critically influence the population representativeness of a clinical study, which further influences the tradeoff between patient safety and research results’ replicability and generalizability. We hypothesize that by integrating patient data, including clinical and genomic data, with public clinical trial information, we can proactively guide investigators to optimize the precision, recruitment feasibility and representativeness of eligibility criteria. This research will demonstrate a novel data-driven and knowledge-based system to assist researchers with optimizing eligibility criteria, through innovative informatics methods for integrating proprietary and public data for deep phenotyping, target population profiling, and quantification and visualization of population representativeness.