Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics

Background The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents. Methods CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO. Results We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice. Conclusions CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number:HI18C2386). KHIDI had no participation in the study design or data collection and analysis process. KHIDI did not participate in the writing of the manuscript

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics

BACKGROUND: The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents. METHODS: CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO. RESULTS: We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice. CONCLUSIONS: CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.ope

Semantic Interoperability in Digital Library Systems

This report is a state-of-the-art overview of activities and research being undertaken in areas relating to semantic interoperability in digital library systems. It has been undertaken as part of the cluster activity of WP5: Knowledge Extraction and Semantic Interoperability (KESI). The authors and contributors draw on the research expertise and experience of a number of organisations (UKOLN, ICS-FORTH, NETLAB, TUC-MUSIC, University of Glamorgan) as well as several work-packages (WP5: Knowledge Extraction and Semantic Interoperability; WP3: Audio-Visual and Non-traditional Objects) within the DELOS2 NoE. In addition, a workshop was held [KESI Workshop Sept. 2004] (co-located with ECDL 2004) in order to provide a forum for the discussion of issues relevant to the topic of this report. We are grateful to those who participated in the forum and for their valuable comments, which have helped to shape this report. Definitions of interoperability, syntactic interoperability and semantic interoperability are presented noting that semantic interoperability is very much about matching concepts as a basis. The NSF Post Digital Libraries Futures Workshop: Wave of the Future [NSF Workshop] has identified semantic interoperability as being of primary importance in digital library research

Community Consensus Guidelines to Support FAIR Data Standards in Clinical Research Studies in Primary Mitochondrial Disease

Primary mitochondrial diseases (PMD) are genetic disorders with extensive clinical and molecular heterogeneity where therapeutic development efforts have faced multiple challenges. Clinical trial design, outcome measure selection, lack of reliable biomarkers, and deficiencies in long-term natural history data sets remain substantial challenges in the increasingly active PMD therapeutic development space. Developing "FAIR" (findable, accessible, interoperable, reusable) data standards to make data sharable and building a more transparent community data sharing paradigm to access clinical research metadata are the first steps to address these challenges. This collaborative community effort describes the current landscape of PMD clinical research data resources available for sharing, obstacles, and opportunities, including ways to incentivize and encourage data sharing among diverse stakeholders. This work highlights the importance of, and challenges to, developing a unified system that enables clinical research structured data sharing and supports harmonized data deposition standards across clinical consortia and research groups. The goal of these efforts is to improve the efficiency and effectiveness of drug development and improve understanding of the natural history of PMD. This initiative aims to maximize the benefit for PMD patients, research, industry, and other stakeholders while acknowledging challenges related to differing needs and international policies on data privacy, security, management, and oversight

The evaluation and harmonisation of disparate information metamodels in support of epidemiological and public health research

BACKGROUND: Descriptions of data, metadata, provide researchers with the contextual information they need to achieve research goals. Metadata enable data discovery, sharing and reuse, and are fundamental to managing data across the research data lifecycle. However, challenges associated with data discoverability negatively impact on the extent to which these data are known by the wider research community. This, when combined with a lack of quality assessment frameworks and limited awareness of the implications associated with poor quality metadata, are hampering the way in which epidemiological and public health research data are documented and repurposed. Furthermore, the absence of enduring metadata management models to capture consent for record linkage metadata in longitudinal studies can hinder researchers from establishing standardised descriptions of consent. AIM: To examine how metadata management models can be applied to ameliorate the use of research data within the context of epidemiological and public health research. METHODS: A combination of systematic literature reviews, online surveys and qualitative data analyses were used to investigate the current state of the art, identify current perceived challenges and inform creation and evaluation of the models. RESULTS: There are three components to this thesis: a) enhancing data discoverability; b) improving metadata quality assessment; and c) improving the capture of consent for record linkage metadata. First, three models were examined to enhance research data discoverability: data publications, linked data on the World Wide Web and development of an online public health portal. Second, a novel framework to assess epidemiological and public health metadata quality framework was created and evaluated. Third, a novel metadata management model to improve capture of consent for record linkage metadata was created and evaluated. CONCLUSIONS: Findings from these studies have contributed to a set of recommendations for change in research data management policy and practice to enhance stakeholders’ research environment