Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

Background: One Digital Health (ODH) aims to propose a framework that merges One Health’s and Digital Health’s specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention. Objectives: This paper aims to elicit how the One Digital Health framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup. Methods: An integrative analysis of the literature was conducted in order to extract instances of the need - or of the eventual already existing deployment - of FAIR principles, for each of the three layers (keys, perspectives, dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework. Results: A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work from, a non-suitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention. Conclusions: ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, towards the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

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“…Globally Unique Identifiers used for the European Platform on Rare Disease Registration (F1 + , F2 + ) 61…”

Section: Resultsmentioning

confidence: 99%

One Digital Health for more FAIRness

Tamburis

Benis

2022

Methods Inf Med

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“…Substantial work has been undertaken to mitigate barriers in the FAIRification process (de Miranda Azevedo and Dumontier, 2020; Jacobsen et al, 2020; Kaliyaperumal et al, 2022; Thompson et al, 2020; Weigel et al, 2020; Wilkinson et al, 2018; Wilkinson et al, 2019) Some institutions, for example, have established dedicated data steward personnel to facilitate data stewardship tasks. A data steward refers to a specialist with expertise in data management, and in the medical research community, they are often skilled in FAIR principles.…”

Section: Discussionmentioning

confidence: 99%

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Rainey¹,

Lutomski

Broeders

2023

Big Data & Society

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FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.

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“…Three out of 15 candidate best papers are with regard to ontology applications. We consider the best paper from Kaliyaperumal et al [8] as an ontology application, since it leverages an ontological model to represent rare disease CDEs and support data integration from distributed data registries.…”

Section: Ontology Applicationsmentioning

confidence: 99%

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

Charlet,

Cui

2023

Yearb Med Inform

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Objectives: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. Methods: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. Results: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. Conclusions: In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.

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Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

Cited by 20 publications

References 11 publications

One Digital Health for more FAIRness

One Digital Health for more FAIRness

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

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