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: 9755488
• 负责人: CHUNHUA WENG
• 金额: $ 60.83万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-14 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755488
• 关键词: Biometry; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Code; Cohort Studies; Communication; Computer Simulation; Data; Data Analytics; Data Reporting; Data Science; Decision Aid; Electronic Health Record; Eligibility Determination; Epigenetic Process; Evaluation; Evidence Based Medicine; Exclusion Criteria; Feasibility Studies; Feedback; Formulation; Glycosylated hemoglobin A; Goals; Grain; Graph; Human; ICD-9; Imagery; Individual; Informatics; Information Dissemination; Knowledge; Knowledge Discovery; Life; Mathematics; Mental disorders; Methods; Minority Recruitment; Non-Insulin-Dependent Diabetes Mellitus; Participant; Patient Recruitments; Patients; Phenotype; Plant Roots; Population; Population Analysis; Positioning Attribute; Public Health; Research; Research Personnel; Sampling Biases; Selection Bias; Semantics; Structure; System; Target Populations; Techniques; Text; base; biomedical informatics; design; genomic data; health disparity; human study; improved; indexing; information organization; innovation; interoperability; knowledge base; novel; patient safety; preservation; recruit; study population; success; text searching; trait

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.

项目概要 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实施1。 资格标准定义了每项人类研究的研究人群，其清晰度、准确性和精确性是明确的。 对于参与者招募、结果传播和证据合成的成功至关重要。 更新是为现实生活中的临床研究人员构建一个数据驱动、基于知识的决策辅助工具来优化 研究资格标准定义。 资格标准语义表示的差异（例如，患有 2 型糖尿病） 其操作化已被定义为临床变量（例如，HbA1C ≥ 6.5% 或 ICD-9 代码 =“250.00”） 作为语义差距2，其缩小对生物医学信息学2、3来说是一个巨大的挑战。 有助于深入理解这种语义差距以及它如何限制计算重用和有效 与临床研究的主要利益相关者沟通资格标准4-9。 通过将自由文本资格标准转变为半结构化格式来帮助弥合这一差距的方法 研究队列识别10-13，相关临床试验的人群代表性分析14-19，文本 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 我们使用其中几种方法开发了一个名为 VITTA17 的可视化系统。 这显示了相关试验中临床试验人群的标准资格和临床特征如何变化。 更重要的是，我们的研究揭示了语义差距的一个未被充分研究的根本原因，那就是 资格标准往往定义不明确、不准确、不具体或不精确，并且不易转化为 现实世界的电子健康记录 (EHR) 数据表示，必须对其标准进行操作。 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 患者通过定义特征来指导和告知数据驱动的资格标准的精确定义25。 对于目标研究人群来说，资格标准严重影响着研究人群的代表性 临床研究，进一步影响患者安全与研究结果可重复性之间的权衡 我们通过将患者数据（包括临床和基因组数据）与公众整合来努力解决这一问题。 临床试验信息，我们可以主动指导研究者优化精准度、招募可行性 这项研究将展示一种新颖的数据驱动和资格标准的代表性。 基于知识的系统，通过创新信息学协助研究人员优化资格标准 整合专有和公共数据以进行深度表型分析、目标人群分析的方法，以及 人口代表性的量化和可视化。