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BACKGROUND: The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. OBJECTIVE: This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best. practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. METHODS: A survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. RESULTS: The study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. CONCLUSIONS: The study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.
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The remedy of memory deficits has been inadequate, as all potential candidates studied thus far have shown limited to no effects and a search for an effective strategy is ongoing. Here, we show that an expression of RGS14414 in rat perirhinal cortex (PRh) produced long-lasting object recognition memory (ORM) enhancement and that this effect was mediated through the upregulation of 14-3-3ζ, which caused a boost in BDNF protein levels and increase in pyramidal neuron dendritic arborization and dendritic spine number. A knockdown of the 14-3-3ζ gene in rat or the deletion of the BDNF gene in mice caused complete loss in ORM enhancement and increase in BDNF protein levels and neuronal plasticity, indicating that 14-3-3ζ-BDNF pathway-mediated structural plasticity is an essential step in RGS14414-induced memory enhancement. We further observed that RGS14414 treatment was able to prevent deficits in recognition, spatial, and temporal memory, which are types of memory that are particularly affected in patients with memory dysfunctions, in rodent models of aging and Alzheimer's disease. These results suggest that 14-3-3ζ-BDNF pathway might play an important role in the maintenance of the synaptic structures in PRh that support memory functions and that RGS14414-mediated activation of this pathway could serve as a remedy to treat memory deficits.
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Córtex Perirrinal , Proteínas 14-3-3/genética , Proteínas 14-3-3/metabolismo , Proteínas 14-3-3/farmacologia , Animais , Fator Neurotrófico Derivado do Encéfalo/metabolismo , Hipocampo/metabolismo , Humanos , Transtornos da Memória/metabolismo , Transtornos da Memória/prevenção & controle , Camundongos , Plasticidade Neuronal/fisiologia , Ratos , Roedores/metabolismoRESUMO
BACKGROUND: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. OBJECTIVE: Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. METHODS: Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. RESULTS: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. CONCLUSIONS: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks.
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Registros Eletrônicos de Saúde , Software , Humanos , Design de Software , Nível Sete de Saúde , Disseminação de InformaçãoRESUMO
BACKGROUND: Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data are managed by dedicated infrastructures called data hubs is essential to facilitating data sharing and reuse. METHODS: To capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was national, European, and global data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022. RESULTS: In total, 41 survey responses received until June 2022 were analysed. Stratification methods were performed to cover the different levels of granularity identified in some data hubs' characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific data governance patterns through the stratifications in terms of the kind of organization (centralized versus decentralized) and role (data controller or data processor) of the health data hub respondents. CONCLUSIONS: The analysis of the responses from health data hub respondents across Europe provided a list of the most frequent aspects, which concluded with a set of specific best practices on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralized way, providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymization methods.
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Confiabilidade dos Dados , Gerenciamento de Dados , Humanos , Coleta de Dados , Europa (Continente) , Instalações de SaúdeRESUMO
The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.
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Mapeamento Encefálico , Longevidade , Idoso , Encéfalo/diagnóstico por imagem , Encéfalo/fisiologia , Criança , Cognição/fisiologia , Função Executiva/fisiologia , Humanos , Imageamento por Ressonância Magnética , Rede Nervosa/fisiologia , Vias Neurais/fisiologiaRESUMO
The consolidation of new memories into long-lasting memories is multistage process characterized by distinct temporal dynamics. However, our understanding on the initial stage of transformation of labile memory of recent experience into stable memory remains elusive. Here, with the use of rats and mice overexpressing a memory enhancer called regulator of G protein signaling 14 of 414 amino acids (RGS14414 ) as a tool, we show that the expression of RGS14414 in male rats' perirhinal cortex (PRh), which is a brain area crucial for object recognition memory (ORM), enhanced the ORM to the extent that it caused the conversion of labile short-term ORM (ST-ORM) expected to last for 40 min into stable long-term ORM (LT-ORM) traceable after a delay of 24 hr, and that the temporal window of 40 to 60 min after object exposure not only was key for this conversion but also was the time frame when a surge in 14-3-3ζ protein was observed. A knockdown of 14-3-3ζ gene abrogated both the increase in 14-3-3ζ protein and the formation of LT-ORM. Furthermore, this 14-3-3ζ upregulation increased brain-derived growth factor (BDNF) levels in the time frame of 60 min and 24 hr and 14-3-3ζ knockdown decreased the BDNF levels, and a deletion of BDNF gene produced loss in mice ability to form LT-ORM. Thus, within 60 min of object exposure, 14-3-3ζ facilitated the conversion of labile ORM into stable ORM, whereas beyond the 60 min, it mediated the consolidation of the stable memory into long-lasting ORM by regulating BDNF signaling.
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Proteínas 14-3-3/biossíntese , Memória de Longo Prazo/fisiologia , Memória de Curto Prazo/fisiologia , Reconhecimento Psicológico/fisiologia , Proteínas 14-3-3/genética , Animais , Fator Neurotrófico Derivado do Encéfalo/deficiência , Fator Neurotrófico Derivado do Encéfalo/genética , Feminino , Humanos , Masculino , Camundongos , Camundongos Knockout , Ratos , Ratos Wistar , Percepção Visual/fisiologiaRESUMO
Cutaneous vasculities (CV) represents a diagnostic challenge, occurs as primary cutaneous disorder or as a manifestation of other entities. Objective: To search the cause of CV. Methods: Patients with CV were prospectively evaluated. In all patients, skin biopsies were drawn, and direct immunofluorescence was done in most of the patients. American College of Rheumatology (ACR) and Chapel Hill Consensus Conference Criteria (CHCC) were used for classification. Results: 32 patients were studied. There was female predominance (71.8%). Children presented drug-associated CV or Schönlein-Henoch púrpura (SHP). Adults presented more frequently SHP, systemic lupus erythematosus or paraneoplastic vasculitis, other diagnosis as polyarteritis nodosa, microscopic polyangiitis, thrombotic vasculitis (post-puerperal), antiphospholipid syndrome, Churg-Strauss syndrome, and drug-associated CV were presented. Using the ACR and CHCC criteria, 50% of cases were classified. Discussion: In our institution, during this work the etiologic diagnostic of CV increased more than twice. However, in the case of HSV or LA and SHP none of the proposed criteria had high specificity; other parameters were used to discern between both. Six patients remained as not classified. In our view, cryoglobulins and hepatitis serology do not seem useful unless patient's history supports they need to be done. Unclassified patients were followed-up closely for 2 years.
Las vasculitis cutáneas (VC), primarias o como manifestación de enfermedades sistémicas, constituyen un reto diagnóstico. Objetivo: Determinar las causas de VC. Métodos: Se incluyeron pacientes con diagnóstico de CV, a los cuales se les realizó valoración clínica, biopsia cutánea y exámenes de laboratorio. En la mayoría de los casos se realizó inmunofluorescencia directa. Los casos se clasificaron con los criterios del American College of Rheumatology (ACR) y la Chapel Hill Consensus Conference (CHCC). Resultados: Se incluyeron 32 pacientes; la frecuencia fue mayor en mujeres (71.8%). Los niños presentaron VC asociadas a medicamentos o púrpura de Schönlein-Henoch (PSH). En adultos se reportó con más frecuencia PSH, vasculitis asociada a lupus eritematoso sistémico y vasculitis paraneoplásicas; otros diagnósticos etiológicos incluyeron poliarteritis nodosa (PAN), poliangeítis microscópica (PAM), vasculitis trombótica (pospuerperal), síndrome antifosfolípidos (SAF), síndrome de Churg-Strauss (SCS) y VC asociada a medicamentos. Utilizando los criterios del ACR y la CHCC para vasculitis se clasificó el 50% de los casos. Discusión: En el Hospital Gea, durante este trabajo, el diagnóstico etiológico de las CV se incrementó más del doble. Sin embargo, en relación a los diagnósticos vasculitis por hipersensibilidad (VHS) y PSH ninguna de las clasificaciones utilizadas contaba con criterios específicos. Seis pacientes permanecieron sin clasificar. Observamos que los estudios de crioglobulinas y serología para hepatitis no son útiles como estudios iniciales, salvo que la historia clínica del paciente lo sugiera. Los pacientes sin clasificar se siguieron por dos años.
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Dermatopatias Vasculares/etiologia , Vasculite/etiologia , Adolescente , Adulto , Idoso , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Adulto JovemRESUMO
Children's own language production has a role in structuring the language of their conversation partners and influences their own development. Children's active participation in their own language development is most apparent in the rich body of work investigating language in natural environments. The advent of automated measures of vocalizations and movement have made such in situ research increasingly feasible. In this chapter, we review recent research on children's language development in context with a particular focus on research employing automated methods in preschool classrooms for children between ages 2 and 5 years. These automated methods indicate that the speech directed to preschool children from specific peers predicts the child's speech to those peers on a subsequent observation occasion. Similar patterns are seen in the influence of peer and teacher phonemic diversity on the phonemic diversity of children's speech to those partners. In both cases, children's own speech to partners was the best predictor of their language abilities, suggesting their active role in their own development. Finally, new research suggests the potential of machine learning to predict children's speech in group contexts, and to transcribe classroom speech to better understand the content of children's conversations and how they change with development.
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Desenvolvimento da Linguagem , Humanos , Pré-Escolar , Fonética , Linguagem Infantil , Interação Social , Instituições Acadêmicas , Grupo Associado , Aprendizado de Máquina , FalaRESUMO
Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.
Traditionally, regional variability in brain signals has been viewed as a source of noise in human neuroimaging research. Our study demonstrates that brain signal variability may contain meaningful information related to psychological processes. We demonstrate that brain signal variability, particularly whole-brain variability, may serve as a reliable indicator of cognitive functions across the life span. Global variability and network-level variability play differing roles in supporting executive functions. Findings suggest that brain signal variability serves as a meaningful indicator of development and cognitive aging.
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Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods: Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results: Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion: The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion: This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.
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Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.
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Registros Eletrônicos de Saúde , Europa (Continente) , Humanos , Interoperabilidade da Informação em Saúde , Cuidados Críticos , Segurança Computacional , SemânticaRESUMO
JOURNAL/nrgr/04.03/01300535-202408000-00038/figure1/v/2023-12-16T180322Z/r/image-tiff Memory deficit, which is often associated with aging and many psychiatric, neurological, and neurodegenerative diseases, has been a challenging issue for treatment. Up till now, all potential drug candidates have failed to produce satisfactory effects. Therefore, in the search for a solution, we found that a treatment with the gene corresponding to the RGS14414 protein in visual area V2, a brain area connected with brain circuits of the ventral stream and the medial temporal lobe, which is crucial for object recognition memory (ORM), can induce enhancement of ORM. In this study, we demonstrated that the same treatment with RGS14414 in visual area V2, which is relatively unaffected in neurodegenerative diseases such as Alzheimer's disease, produced long-lasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer's disease. Furthermore, we found that the prevention of memory deficits was mediated through the upregulation of neuronal arborization and spine density, as well as an increase in brain-derived neurotrophic factor (BDNF). A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer's disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits. These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer's disease. Therefore, our findings of RGS14414 gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.
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BACKGROUND: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. OBJECTIVE: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. METHODS: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (20
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The objective of this study, as part of the European HealthyCloud project, has been to analyse the data management mechanisms of representative data hubs in Europe and identify whether they comply with an adequate adoption of FAIR principles that will enable data discovery. A dedicated consultation survey was performed, and the analysis of the results allowed to generate a set of comprehensive recommendations and best practices so that these data hubs can be integrated into a data sharing ecosystem such as the future European Health Research and Innovation Cloud.
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Gerenciamento de Dados , Ecossistema , Europa (Continente) , Encaminhamento e ConsultaRESUMO
Background: The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes. Objective: This paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution. Methods: To analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed. Results: In the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used. Conclusions: Adopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques.
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The COVID-19 pandemic may have exacerbated depression, anxiety, and executive function (EF) difficulties in children with autism spectrum disorder (ASD). EF skills have been positively associated with mental health outcomes. Here, we probed the psychosocial impacts of pandemic responses in children with and without ASD by relating pre-pandemic EF assessments with anxiety and depression symptoms several months into the pandemic. We found that pre-pandemic inhibition and shifting difficulties, measured by the Behavior Rating Inventory of Executive Function, predicted higher risk of anxiety symptoms. These findings are critical for promoting community recovery and maximizing clinical preparedness to support children at increased risk for adverse psychosocial outcomes.
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[This corrects the article DOI: 10.2196/27990.].
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BACKGROUND: Clinical Practice Guidelines (CPGs) provide healthcare professionals with performance and decision-making support during the treatment of patients. Sometimes, however, they are poorly implemented. The IDE4ICDS platform was developed and validated with CPGs for type 2 diabetes mellitus (T2DM). OBJECTIVE: The main objective of this paper is to present the results of the clinical validation of the IDE4ICDS platform in a real clinical environment at two health clinics in the Andalusian Public Health System (SSPA) in the southern Spanish region of Andalusia. METHODS: National and international knowledge sources on T2DM were selected and reviewed and used to define a diabetes CPG model on the IDE4ICDS platform. Once the diabetes CPG was configured and deployed, it was validated. A total of 506 patients were identified as meeting the inclusion criteria, of whom 130 could be recruited and 89 attended the appointment. RESULTS: A concordance analysis was performed with the kappa value. Overall agreement between the recommendations provided by the system and those recorded in each patient's EHR was good (0.61 - 0.80) with a total kappa index of 0.701, leading to the conclusion that the system provided appropriate recommendations for each patient and was therefore well-functioning. CONCLUSIONS: A series of possible improvements were identified based on the limitations for the recovery of variables related to the quality of these recolected variables, the detection of duplicate recommendations based on different input variables for the same patient, and clinical usability, such as the capacity to generate reports based on the recommendations generated. Nevertheless, the project resulted in the IDE4ICDS platform: a Clinical Decision Support System (CDSS) capable of providing appropriate recommendations for improving the management and quality of patient care and optimizing health outcomes. The result of this validation is a safe and effective pathway for developing and adopting digital transformation at the regional scale of the use of biomedical knowledge in real healthcare.
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Sistemas de Apoio a Decisões Clínicas , Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/terapia , Atenção à Saúde , RegistrosRESUMO
BACKGROUND: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE: The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS: A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS: This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS: An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.
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In the EU project FAIR4Health, a ETL pipeline for the FAIRification of structured health data as well as an agent-based, distributed query platform for the analysis of research hypotheses and the training of machine learning models were developed. The system has been successfully tested in two clinical use cases with patient data from five university hospitals. Currently, the solution is also being considered for use in other hospitals. However, configuring the system and deploying it in the local IT architecture is non-trivial and meets with understandable concerns about security. This paper presents a model for describing the information architecture based on a formal approach, the 3LGM metamodel. The model was evaluated by the developers. As a result, the clear separation of tasks and the software components that implement them as well as the rich description of interactions via interfaces were positively emphasized.