caCDE-QA: A Quality Assurance Platform for Cancer Study Common Data Elements

caCDE-QA：癌症研究通用数据元素的质量保证平台

• 批准号: 9110905
• 负责人: Guoqian Jiang
• 金额: $ 32.92万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9110905
• 关键词: Address; Adopted; Algorithms; Clinical; Clinical Data; Clinical Research; Collaborations; Common Data Element; Communities; Community Services; Data; Data Element; Data Quality; Data Storage and Retrieval; Detection; Dictionary; Face; Fostering; Goals; Health; Life Cycle Stages; Malignant Neoplasms; Metadata; Methods; Modeling; National Cancer Institute; Network-based; Online Systems; Outcome; Performance; Publishing; Research; Semantics; Services; Standardization; Structure; Technology; Testing; The Cancer Genome Atlas; Unified Medical Language System; Validation; base; computer based Semantic Analysis; cost; data sharing; design; information model; novel; quality assurance; repository; research study; tool; usability; web portal

DESCRIPTION (provided by applicant): Domain-specific common data elements (CDEs) are emerging as an effective approach to standards-based clinical research data storage and retrieval and have been broadly adopted. For example, the National Cancer Institute (NCI) created the Cancer Data Standards Repository (caDSR) based on the ISO/IEC 11179 standard for metadata repositories. However, cancer clinical research community faces significant challenges related to scalability, governance, and data quality for CDE modeling. In particular, the lack of robust, principled and automated QA algorithms contributes to CDE content errors that can have a significant negative impact on downstream CDE uses. Our overall goal is to build a novel quality assurance (QA) framework to overcome methodological and computational challenges with respect to error detection in the modeling of common data elements (CDEs), recognition of duplicates or similar CDEs, and CDE usability, thereby producing high-quality CDEs for cancer clinical research studies. Our proposed approach is to design, develop and evaluate an integrative platform known as caCDE-QA that implements a suite of QA tools to audit experimental cancer study CDEs represented in a semantic web framework, deploying a QA web-portal with standard semantic services for community collaboration. Our specific aims are: (1) To develop a suite of QA tools for validation and harmonization of cancer study CDEs. (2) To apply the QA tools to audit experimental cancer study CDEs represented in a semantic web framework. We will also evaluate the performance of the QA tools in terms of efficiency, accuracy and usability by comparing with the baseline tools that exist in the NCI caDSR and CIMI communities. (3) To deploy and evaluate a QA web-portal for collaborative CDE review and harmonization. We will coordinate community-based efforts soliciting requirements regarding cancer study CDE discovery and harmonization and fostering a specification of the common data element services (CDES) standard. We will disseminate and test the newly developed QA methods and tools in collaboration with the Clinical Data Interchange Standards Consortium (CDISC) and CIMI Communities. This project will contribute novel QA methods and tools for validation and semantic harmonization of cancer study CDEs. This is of great significance in that it will be enabling efficient CDE modeling and producing high-quality reusable CDEs, which are critical for facilitating cancer clinical research data sharing and accelerating systematic clinical outcomes capturing.

描述（由申请人提供）：特定领域的通用数据元素（CDE）正在成为基于标准的临床研究数据存储和检索的有效方法，并已被广泛采用。例如，美国国家癌症研究所（NCI）基于ISO/IEC 11179元数据存储库标准创建了癌症数据标准存储库（caDSR）。然而，癌症临床研究社区面临着与CDE建模的可扩展性、治理和数据质量相关的重大挑战。特别是，缺乏强大的，原则性的和自动化的QA算法有助于CDE内容错误，这可能对下游CDE的使用产生重大的负面影响。我们的总体目标是建立一个新的质量保证（QA）框架，以克服方法和计算方面的挑战，在建模的常见数据元素（CDE）的错误检测，识别重复或类似的CDE，和CDE的可用性，从而产生高质量的CDE癌症临床研究。我们提出的方法是设计，开发和评估一个集成平台，称为caCDE-QA，实现了一套QA工具，以审计实验癌症研究CDE中表示的语义网络框架，部署一个QA门户网站与标准的语义服务的社区协作。我们的具体目标是：（1）开发一套用于验证和协调癌症研究CDE的QA工具。(2)应用QA工具审核语义网框架中表示的实验性癌症研究CDE。我们还将通过与NCI caDSR和CIMI社区中存在的基线工具进行比较，评估QA工具在效率，准确性和可用性方面的性能。(3)部署和评估质量保证门户网站，以进行CDE协作审查和协调。我们将协调以社区为基础的努力，征求有关癌症研究CDE发现和协调的要求，并促进通用数据元素服务（CDES）标准的规范。我们将与临床数据交换标准联盟（CDISC）和CIMI社区合作，传播和测试新开发的QA方法和工具。该项目将为癌症研究CDE的验证和语义协调提供新的QA方法和工具。这具有重要意义，因为它将实现高效的CDE建模并产生高质量的可重用CDE，这对于促进癌症临床研究数据共享和加速系统性临床结果捕获至关重要。

项目成果

Automated Population of an i2b2 Clinical Data Warehouse using FHIR

• 发表时间: 2018
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: H. Solbrig;Na Hong;S. Murphy;Guoqian Jiang

Developing a Standards-Based Information Model for Representing Computable Diagnostic Criteria: A Feasibility Study of the NQF Quality Data Model.

开发基于标准的信息模型来表示可计算的诊断标准：NQF 质量数据模型的可行性研究。

• 发表时间: 2015
• 期刊: Studies in health technology and informatics
• 作者: Jiang,Guoqian;Solbrig,HaroldR;Pathak,Jyotishman;Chute,ChristopherG
• 通讯作者: Chute,ChristopherG

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

用于挖掘癌症临床试验中基因突变的基于语义网络的系统。

• 发表时间: 2015
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Priya,Sambhawa;Jiang,Guoqian;Dasari,Surendra;Zimmermann,MichaelT;Wang,Chen;Heflin,Jeff;Chute,ChristopherG
• 通讯作者: Chute,ChristopherG

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner.

使用人工神经网络以半自动方式将癌症常见数据元素映射到生物医学研究集成域组模型。

• DOI: 10.1186/s12911-019-0979-5
• 发表时间: 2019
• 期刊: BMC medical informatics and decision making
• 影响因子: 3.5
• 作者: Renner,Robinette;Li,Shengyu;Huang,Yulong;vanderZijp-Tan,AdaChaeli;Tan,Shaobo;Li,Dongqi;Kasukurthi,MohanVamsi;Benton,Ryan;Borchert,GlenM;Huang,Jingshan;Jiang,Guoqian
• 通讯作者: Jiang,Guoqian

Modeling and validating HL7 FHIR profiles using semantic web Shape Expressions (ShEx).

使用语义网络形状表达式 (ShEx) 建模和验证 HL7 FHIR 配置文件。

• DOI: 10.1016/j.jbi.2017.02.009
• 发表时间: 2017-03
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Solbrig HR;Prud'hommeaux E;Grieve G;McKenzie L;Mandel JC;Sharma DK;Jiang G
• 通讯作者: Jiang G