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: 9332989
• 负责人: CHUNHUA WENG
• 金额: $ 60.37万
• 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/9332989
• 关键词: Biometry; Cereals; 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; 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; Patients; Phenotype; Plant Roots; Population; Population Analysis; Positioning Attribute; Public Health; Recruitment Activity; 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; 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.

项目摘要 我们的长期目标是使用Informates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。他们的清晰度，准确性和精度是 对于参与者招募，结果传播和证据综合的成功至关重要。我们的目标 更新将为现实生活中的临床研究人员建立一个基于数据驱动和知识的决策援助，以优化 研究资格标准定义。 资格标准的语义表示的差异（例如，患有2型糖尿病） 及其作为临床变量的操作（例如，HBA1C≥6.5％或ICD-9代码='250.00'）已被定义 作为语义GAP2，其结束是生物医学信息的巨大挑战2,3。我们的研究有 有助于对这一语义差距的深入理解以及它如何限制计算重用有效 将资格标准与临床研究的主要利益相关者的沟通4-9。我们已经开发了信息 通过将自由文本资格标准转换为半结构格式来帮助弥合这一差距的方法 研究队列鉴定10-13，对代表相关临床试验的人群的分析14-19，文本 挖掘常见资格特征及其趋势18,20-24，并确定可疑的排除标准 用于精神障碍试验25。我们使用了其中几种方法来开发一个名为Vitta17的可视化系统 这表明了资格标准和临床试验人群的临床特征如何在相关试验中有所不同。 更重要的是，我们的研究揭示了语义差距的理解根本原因，那就是 资格标准通常定义不明，不准确，非特异性或浸渍，不容易转化为 必须对标准进行操作的现实世界电子健康记录（EHR）数据表示。这 大型患者数据的出现为借鉴了现实世界的特征提供了前所未有的机会 指导并告知数据驱动的资格标准的精确定义25。通过定义特征 在预期的研究人群中，符合条件的标准严重影响了代表的人群 临床研究，进一步影响了患者安全和研究结果的可复制性和 概括性。我们通过将包括临床和基因组数据在内的患者数据与公众整合到包括临床和基因组数据在内的患者数据来假设 临床试验信息，我们可以主动指导研究人员优化精确性，招聘可行性 和资格标准的代表性。这项研究将证明一种新颖的数据驱动和 通过创新信息，基于知识的系统，可帮助研究人员获得优化的资格标准 整合专有和公共数据的方法，用于深度表型，目标人群概况以及 人口代表性的数量和可视化。