EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.

BACKGROUND: Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs). METHODS: A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated. RESULTS: Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice. CONCLUSION: These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

Real-World Evidence to Reinforce Clinical Trial Evidence in Health Technology Assessment: A Critical Review of Real-World Evidence Requirements from Seven Countries and Recommendations to Improve Acceptance.

BACKGROUND: Real-world evidence (RWE) can reinforce clinical trial evidence in health technology assessment (HTA). OBJECTIVES: Review HTA bodies' (HTAbs) requirements for RWE, real uses, and acceptance across seven countries (Brazil, Canada, France, Germany, Italy, Spain, and the United Kingdom) and outline recommendations that may improve acceptance of RWE in efficacy/effectiveness assessments and appraisals processes. METHODS: RWE requirements were summarized based on HTAbs' guidelines. Acceptance by HTAbs was evaluated based on industry experience and case studies. RESULTS: As of June 2022, RWE methodological guidelines were in place in three of the seven countries. HTAbs typically requested analyses based on local data sources, but the preferred study design and data sources differed. HTAbs had individual submission, assessment, and appraisal processes; some allowed early meetings for the protocol and/or results validation, though few involved external experts or medical societies to provide input to assessment and appraisal. The extent of submission, assessment, and appraisal requirements did not necessarily reflect the degree of acceptance. CONCLUSION: All the countries reviewed face common challenges regarding the use of RWE. Our proposals address the need to facilitate collaboration and communication with industry and regulatory agencies and the need for specific guidelines describing RWE design and criteria of acceptance throughout the assessment and appraisal processes.

Data Definition Ontology for clinical data integration and querying.

This paper describes an approach to build a Data Definition Ontology (DDO) in the context of full domain ontology integration with datasets in order to share and query clinical heterogeneous data repositories. We have adapted an existing semantic web tool (D2RQ) to implement a process that automatically generates the DDO from a database information model, thanks to reverse engineering and schema mapping approaches. This study has been performed in the context of the DebugIT European project (Detecting and Eliminating Bacteria UsinG Information Technology) that aims to control and monitor the bacterial growth via a semantic interoperability platform (IP). The evaluation of the process is based, first, on the accuracy of the produced DDO for different samples of database storage and second, by checking the congruency between the DDO and the D2RQ database mapping file.

Interoperability driven integration of biomedical data sources.

In this paper, we introduce a data integration methodology that promotes technical, syntactic and semantic interoperability for operational healthcare data sources. ETL processes provide access to different operational databases at the technical level. Furthermore, data instances have they syntax aligned according to biomedical terminologies using natural language processing. Finally, semantic web technologies are used to ensure common meaning and to provide ubiquitous access to the data. The system's performance and solvability assessments were carried out using clinical questions against seven healthcare institutions distributed across Europe. The architecture managed to provide interoperability within the limited heterogeneous grid of hospitals. Preliminary scalability result tests are provided.

The Information Quality Triangle: a methodology to assess clinical information quality.

Building qualitative clinical decision support or monitoring based on information stored in clinical information (or EHR) systems cannot be done without assessing and controlling information quality. Numerous works have introduced methods and measures to qualify and enhance data, information models and terminologies quality. This paper introduces an approach based on an Information Quality Triangle that aims at providing a generic framework to help in characterizing quality measures and methods in the context of the integration of EHR data in a clinical datawarehouse. We have successfully experimented the proposed approach at the HEGP hospital in France, as part of the DebugIT EU FP7 project.

Ensuring HL7-based information model requirements within an ontology framework.

This paper describes the building of an HL7-based Information Model Ontology (IMO) that can be exploited by a domain ontology in order to distribute querying over different clinical data repositories. We employed the Open Medical Development Framework (OMDF) based on a model driven development methodology. OMDF provides model transformation features to build an HL7-based information model that covers the conceptual scope of a target project. The resulting IMO is used to mediate between ontologically queries and information retrieval from semantically less defined Hospital Information Systems (HIS). In the context of the DebugIT project - which scope corresponds to the control of infectious diseases and antimicrobial resistances - Information Model Ontology is integrated to the DebugIT domain ontology in order to express queries.

The DebugIT core ontology: semantic integration of antibiotics resistance patterns.

Antibiotics resistance development poses a significant problem in today's hospital care. Massive amounts of clinical data are being collected and stored in proprietary and unconnected systems in heterogeneous format. The DebugIT EU project promises to make this data geographically and semantically interoperable for case-based knowledge analysis approaches aiming at the discovery of patterns that help to align antibiotics treatment schemes. The semantic glue for this endeavor is DCO, an application ontology that enables data miners to query distributed clinical information systems in a semantically rich and content driven manner. DCO will hence serve as the core component of the interoperability platform for the DebugIT project. Here we present DCO and an approach thet uses the semantic web query language SPARQL to bind and ontologically query hospital database content using DCO and information model mediators. We provide a query example that indicates that ontological querying over heterogeneous information models is feasible via SPARQL construct- and resource mapping queries.

Biomedical data management: a proposal framework.

The expansion of biomedical knowledge, reductions in computing costs and spread of IT facilities have led to an explosion of the biomedical electronic data. However, these data are rarely integrated and analysed because of lack of tools. The integration process is complex due to technical and semantic heterogeneity as well as lack of reliability in such distributed system. In addition, for the specific case of biomedical data, privacy is a crucial constraint. This paper presents a pilot system that will be used in the European FP7 DebugIT project to integrate biomedical data from several healthcare centres across Europe.

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.

Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine.

This article is a position paper dealing with semantic interoperability challenges. It addresses the Variety and Veracity dimensions when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to interoperability standards. We discuss how semantics can contribute to the improvement of information sharing and address the problem of data mediation with domain ontologies. We then introduce the main steps for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in standardisation and the importance of knowledge formalization.

Federating patients identities: the case of rare diseases.

BACKGROUND: Patient information in rare disease registries is generally collected from numerous data sources, necessitating the data to be federated. In addition, data for research purposes must be de-identified. Transforming nominative data into de-identified data is thus a key issue, while minimizing the number of identity duplicates. We propose a method enabling patient identity federation and rare disease data de-identification while preserving the pertinence of the provided data. RESULTS: We developed a rare disease patient identifier. The IdMR generation process is a three-phased algorithm involving a hash function to irreversibly de-identify nominative patient data, including those of foetuses. This process minimizes collision risks and reduces variability for the purpose of identity federation. The IdMR was generated for 360,000 patients of the CEMARA database. It allowed identity federation of 1771 duplicated files. No collisions were introduced. CONCLUSION: We examined and discussed the risks of collisions and the creation of duplicates as well as the risks of patient re-identification. We discussed our choice of nominative input information in light of that used by other patient identification solutions. The IdMR is a patient identifier that enables identity federation and file linkage. The simplicity of the algorithm and the universality and stability of the input data make it a good candidate for European cross-border rare disease projects.

Management of rare diseases of the Head, Neck and Teeth: results of a French population-based prospective 8-year study.

BACKGROUND: In the last ten years, national rare disease networks have been established in France, including national centres of expertise and regional ones, with storage of patient data in a bioinformatics tool. The aim was to contribute to the development and evaluation of health strategies to improve the management of patients with rare diseases. The objective of this study has been to provide the first national-level data concerning rare diseases of the head, neck and teeth and to assess the balance between demand and supply of care in France. METHODS: Centres of expertise for rare diseases record a minimum data set on their clinical cases, using a list of rare Head, Neck and Teeth diseases established in 2006. The present analysis focuses on 2008 to 2015 data based on the Orphanet nomenclature. Each rare disease RD "case" was defined by status "affected" and by the degree of diagnostic certainty, encoded as: confirmed, probable or non-classifiable. Analysed parameters, presented with their 95% confidence intervals using a Poisson model, were the following: time and age at diagnosis, proportions of crude and standardized RD prevalence by age, gender and geographical site. The criteria studied were the proportions of patients in Paris Region and the "included cases geography", in which these proportions were projected onto the other French Regions, adjusting for local populations. RESULTS: In Paris Region, estimated prevalence of these diseases was 5.58 per 10,000 inhabitants (95% CI 4.3-7.1). At December 31st 2015, 11,342 patients were referenced in total in France, of whom 7294 were in Paris Region. More than 580 individual clinical entities (ORPHA code) were identified with their respective frequencies. Most abnormalities were diagnosed antenatally. Nearly 80% of patients recorded come to Paris hospitals to obtain either diagnosis, care or follow up. We observed that the rarer the disease, the more patients were referred to Paris hospitals. CONCLUSIONS: A health network covering a range of aspects of the rare diseases problematic from diagnostics to research has been developed in France. Despite this, there is still a noticeable imbalance between health care supply and demand in this area.

Prevalence of fibrodysplasia ossificans progressiva (FOP) in France: an estimate based on a record linkage of two national databases.

BACKGROUND: Fibrodysplasia ossificans progressiva (FOP) is a rare, severely disabling, and life-shortening genetic disorder that causes the formation of heterotopic bone within soft connective tissue. Previous studies found that the FOP prevalence was about one in every two million lives. The aim of this study is to estimate the FOP prevalence in France by probabilistic record-linkage of 2 national databases: 1) the PMSI (Programme de médicalisation des systèmes d'information), an administrative database that records all hospitalization activities in France and 2) CEMARA, a registry database developed by the French Centres of Reference for Rare Diseases. RESULTS: Using a capture-recapture methodology to adjust the crude number of patients identified in both data sources, 89 FOP patients were identified, which results in a prevalence of 1.36 per million inhabitants (CI95% = [1.10; 1.68]). FOP patients' mean age was 25 years, only 14.9% were above 40 years, and 53% of them were males. The first symptoms - beside toe malformations- occurred after birth for 97.3% of them. Mean age at identified symptoms was 7 years and above 18 years for only 6.9% of patients. Mean age at diagnosis was 10 years, and above 18 years for 14.9% of the patients. FOP patients were distributed across France. CONCLUSIONS: Despite the challenge of ascertaining patients with rare diseases, we report a much higher prevalence of FOP in France than in previous studies elsewhere. We suggest that efforts to identify patients and confirm the diagnosis of FOP should be reinforced and extended at both national and European level.

Towards data integration automation for the French rare disease registry.

Building a medical registry upon an existing infrastructure and rooted practices is not an easy task. It is the case for the BNDMR project, the French rare disease registry, that aims to collect administrative and medical data of rare disease patients seen in different hospitals. To avoid duplicating data entry for health professionals, the project plans to deploy connectors with the existing systems to automatically retrieve data. Given the data heterogeneity and the large number of source systems, the automation of connectors creation is required. In this context, we propose a methodology that optimizes the use of existing alignment approaches in the data integration processes. The generated mappings are formalized in exploitable mapping expressions. Following this methodology, a process has been experimented on specific data types of a source system: Boolean and predefined lists. As a result, effectiveness of the used alignment approach has been enhanced and more good mappings have been detected. Nonetheless, further improvements could be done to deal with the semantic issue and process other data types.

LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems.

Characterizing a rare disease diagnosis for a given patient is often made through expert's networks. It is a complex task that could evolve over time depending on the natural history of the disease and the evolution of the scientific knowledge. Most rare diseases have genetic causes and recent improvements of sequencing techniques contribute to the discovery of many new diseases every year. Diagnosis coding in the rare disease field requires data from multiple knowledge bases to be aggregated in order to offer the clinician a global information space from possible diagnosis to clinical signs (phenotypes) and known genetic mutations (genotype). Nowadays, the major barrier to the coding activity is the lack of consolidation of such information scattered in different thesaurus such as Orphanet, OMIM or HPO. The Linking Open data for Rare Diseases (LORD) web portal we developed stands as the first attempt to fill this gap by offering an integrated view of 8,400 rare diseases linked to more than 14,500 signs and 3,270 genes. The application provides a browsing feature to navigate through the relationships between diseases, signs and genes, and some Application Programming Interfaces to help its integration in health information systems in routine.

A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research.

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.

National registries of rare diseases in Europe: an overview of the current situation and experiences.

The European Union (EU) policy for healthcare requires the establishment of a system of European Reference Networks, union-wide information databases, and registries for rare diseases (RDs) based on shared criteria. In pursuing its goals, the 'Building Consensus and Synergies for the EU Registration of RD Patients in Europe' (EPIRARE) project convened a meeting with experts of the competent health authorities to discuss the role of national institutional RD patient registries in supporting EU patient registration and the room for international cooperation. With this aim, this paper comparatively analyses the current situation of national institutional RD registries in the EU.

Formalizing mappings to optimize automated schema alignment: application to rare diseases.

In the era of data sharing and systems interoperability, the automation of data schema alignment has become a priority. Discovering data mappings is the aim of many alignment approaches that have been described in the literature and the effectiveness of which depends on data specifications. In this context, we propose a method for mappings formalization that allows automated data integration processes optimization. This method, involving both data element level and value element level, allows an automated inference of mappings expressed by rules. In this paper, we start by describing the methods used to achieve this mappings formalization. Then, we explain how it has been validated by characterizing data from two use cases. We end up by discussing the objectives of the proposed formalization.

An ontological approach for the exploitation of clinical data.

Clinical data captured in hospital information systems may be unusable in their original format due to missing information or knowledge. The use of external resources (e.g. domain ontology) could be a way of dealing with this lack of knowledge. Our study thus aimed to develop a framework allowing a user to perform medical queries in the context of infectious diseases. By creating an interaction between a knowledge source and clinical data, using semantic and semantic web tools and methods, the users are able to perform queries on a database to obtain results about antibiotic resistance. This work has been performed in the context of the DebugIT European project that aims to control and monitor the antibioresistance growth via a semantic interoperability platform. The results obtained by the use of different semantic web tools were quantitatively evaluated by comparison of the number of results and the query execution time. We have compared our approach with classic business intelligence approaches in terms of usability and functionality.