scholarly journals Domain-specific Common Data Elements for Rare Disease Registration: A Conceptual Approach of a European Joint Initiative towards Semantic Interoperability in Rare Disease Research (Preprint)

10.2196/32158 ◽  
2021 ◽  
Author(s):  
Haitham Abaza ◽  
Dennis Kadioglu ◽  
Simona Martin ◽  
Andri Papadopoulou ◽  
Bruna dos Santos Viera ◽  
...  
2021 ◽  
Author(s):  
Rajaram Kaliyaperumal ◽  
Mark D Wilkinson ◽  
Pablo Alarcon Moreno ◽  
Nirupama Benis ◽  
Ronald Cornet ◽  
...  

Background: The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Disease (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results: Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions: Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.


2014 ◽  
Vol 22 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Rémy Choquet ◽  
Meriem Maaroufi ◽  
Albane de Carrara ◽  
Claude Messiaen ◽  
Emmanuel Luigi ◽  
...  

Abstract Background Although rare disease patients make up approximately 6–8% of all patients in Europe, it is often difficult to find the necessary expertise for diagnosis and care and the patient numbers needed for rare disease research. The second French National Plan for Rare Diseases highlighted the necessity for better care coordination and epidemiology for rare diseases. A clinical data standard for normalization and exchange of rare disease patient data was proposed. The original methodology used to build the French national minimum data set (F-MDS-RD) common to the 131 expert rare disease centers is presented. Methods To encourage consensus at a national level for homogeneous data collection at the point of care for rare disease patients, we first identified four national expert groups. We reviewed the scientific literature for rare disease common data elements (CDEs) in order to build the first version of the F-MDS-RD. The French rare disease expert centers validated the data elements (DEs). The resulting F-MDS-RD was reviewed and approved by the National Plan Strategic Committee. It was then represented in an HL7 electronic format to maximize interoperability with electronic health records. Results The F-MDS-RD is composed of 58 DEs in six categories: patient, family history, encounter, condition, medication, and questionnaire. It is HL7 compatible and can use various ontologies for diagnosis or sign encoding. The F-MDS-RD was aligned with other CDE initiatives for rare diseases, thus facilitating potential interconnections between rare disease registries. Conclusions The French F-MDS-RD was defined through national consensus. It can foster better care coordination and facilitate determining rare disease patients’ eligibility for research studies, trials, or cohorts. Since other countries will need to develop their own standards for rare disease data collection, they might benefit from the methods presented here.


2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Domenica Taruscio ◽  
Emanuela Mollo ◽  
Sabina Gainotti ◽  
Manuel Posada de la Paz ◽  
Fabrizio Bianchi ◽  
...  

2020 ◽  
Author(s):  
Hye Hyeon Kim ◽  
Yu Rang Park ◽  
Ju Han Kim

Abstract Background: Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents. Methods: We addressed these limitations by defining three new types of semantic relationship ( dependency , composite , and variable ) in our previous studies. The present study created new and further extended existing semantic types ( hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered , operated , required , and dependent . For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints. Results: All of the CDEs ( n =1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n=238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity. Conclusion: MDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.


2019 ◽  
Author(s):  
Hye Hyeon Kim ◽  
Yu Rang Park ◽  
Ju Han Kim

Abstract Background Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents. Methods We addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study further extended semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints. Results All of the CDEs (n=1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 606 CDEs (259 atomic and 20 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity. Conclusion MDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics and improved semantic interoperability without semantic loss.


Author(s):  
Latha Ganti Stead ◽  
◽  
Aakash N Bodhit ◽  
Pratik Shashikant Patel ◽  
Yasamin Daneshvar ◽  
...  

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Monique F Kilkenny ◽  
Helen M Dewey ◽  
Natasha A Lannin ◽  
Vijaya Sundararajan ◽  
Joyce Lim ◽  
...  

Introduction: Multiple data collections can be a burden for clinicians. In 2009, the Australian Stroke Clinical Registry (AuSCR) was established by non-government and research organizations to provide quality of care data unavailable for acute stroke admissions. We show here the reliability of linking complimentary registry data with routinely collected hospital discharge data submitted to governmental bodies. Hypothesis: A high quality linkage with a > 90% rate is possible, but requires multiple personal identifiers common to each dataset. Methods: AuSCR identifying variables included date of birth (DoB), Medicare number, first name, surname, postcode, gender, hospital record number, hospital name and admission date. The Victorian Department of Health emergency department (ED) and hospital discharge linked dataset has most of these, with first name truncated to the first 3 digits, but no surname. Common data elements of AuSCR patients registered at a large hospital in Melbourne, Victoria (Australia) between 15 June 2009 and 31 December 2010 were submitted to undergo stepwise deterministic linkage. Results: The Victorian AuSCR sample had 818 records from 788 individuals. Three steps with 1) Medicare number, postcode, gender and DoB (80% matched); 2) hospital number/admit date; and 3) ED number/visit date were required to link AuSCR data with the ED and hospital discharge data. These led to an overall high quality linkage of >99% (782/788) of AuSCR patients, including 731/788 for ED records and 736/788 for hospital records. Conclusion: Multiple personal identifiers from registries are required to achieve reliable linkage to routinely collected hospital data. Benefits of these linked data include the ability to investigate a broader range of research questions than with a single dataset. Characters with spaces= 1941 (limit is 1950)


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