Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research.

BACKGROUND: Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that "health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements". OBJECTIVE: To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). DATA AND METHODS: This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. RESULTS: The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. CONCLUSION: The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

Using a nonparametric item response theory model to identify patterns of cognitive decline: The Mokken scale analysis.

Cognitive decline, particularly in dementia, presents complex challenges in early detection and diagnosis. While Item Response Theory (IRT) has been instrumental in identifying patterns of cognitive impairment through psychometric tests, its parametric models often require large sample sizes and strict assumptions. This creates a need for more adaptable, less demanding analytical methods. This study aimed to evaluate the effectiveness of Mokken scale analysis (MSA), a nonparametric IRT model, in identifying hierarchical patterns of cognitive impairment from psychometric tests. Using data from 1164 adults over 60 years old, we applied MSA to the orientation subscale of ACE-III. Our analysis involved calculating scalability, monotone homogeneity, invariant item ordering (IIO) and response functions. The MSA effectively retrieved the hierarchical order of cognitive impairment patterns. Most items showed strong scalability and consistent patterns of cognitive performance. However, challenges with IIO were observed, particularly with items having adjacent difficulty parameters. The findings highlight MSA's potential as a practical alternative to parametric IRT models in cognitive impairment research. Its ability to provide valuable insights into patterns of cognitive deterioration, coupled with less stringent requirements, makes it a useful tool for clinicians and researchers.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

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 (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. RESULTS: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. CONCLUSIONS: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Privacy-preserving federated machine learning on FAIR health data: A real-world application.

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.

A prospective observational concordance study to evaluate computational model-driven clinical practice guidelines for Type 2 diabetes mellitus.

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.

"Are we in this together?": embedding social identity detection in drones improves emergency coordination.

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders' decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors' talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.

Metaanálisis de generalización de la fiabilidad de los mitos de violación / Meta-analysis of reliability generalization of the rape myths

Resumen Los mitos de violación son actitudes y creencias generalmente falsas, amplias y persistentes, acerca de la violación, la víctima y el agresor, que son utilizadas para negar o justificar la agresión sexual hacia las mujeres. En las últimas dos décadas, los instrumentos más utilizados para medir este constructo corresponden a la escala de aceptación de mitos de violación de Illinois (IRMAS), que utiliza expresiones directas y explícitas mediante un lenguaje clásico, y la escala de aceptación de mitos modernos de agresión sexual (AMMSA) que usa un lenguaje sutil, indirecto y moderno. Se realizó un metaanálisis de generalización de la fiabilidad de 69 estudios empíricos que utilizaron alguna de las dos escalas de mitos de violación. El objetivo fue estimar la fiabilidad media de las puntuaciones combinadas de las escalas IRMAS y AMMSA para obtener un valor aproximado de su fiabilidad general y evaluar el posible efecto moderador de algunas variables de interés. El promedio de la fiabilidad por consistencia interna de las puntuaciones de las escalas para las 98 muestras estudiadas fue de .85, IC95 % [.84, .86]. Se observó una alta heterogeneidad (I. = 96 %), y el número de ítems es la única variable moderadora que explica significativamente la variabilidad de la fiabilidad observada. Estos resultados muestran que ambas escalas presentan índices de consistencia interna aceptables en sus diversas aplicaciones. Por lo tanto, las medidas de aceptación de mitos de violación cumplen con los criterios de fiabilidad adecuados para ser utilizadas en investigaciones empíricas en distintos contextos.

Abstract Rape myths are widespread and persistent attitudes, beliefs, and stereotypes, usually false, about rape, the victim, and the perpetrator. Their function is to deny and justify sexual assaults against women, affecting the victim's attributions of responsibility and the perpetrator's attributions of guilt in rape cases. These myths exert a bias in the processing of information, directing attention and perception toward stimuli that justify the victim's responsibility for sexual aggression. These beliefs can be grouped into several types of myths: Myths that hold the victim responsible by arguing that women should be careful and not expose themselves to avoid sexual aggression, myths that justify and reduce the responsibility of the aggressor by stating that the man could not contain his sexual desire and those myths that deny or normalize sexual aggression, which propose that rape occurs only in very specific contexts. In the last two decades, the instruments most commonly used to measure these beliefs are The Illinois Rape Myth Acceptance Scale (IRMAS), which uses direct and explicit expressions through classic language, and the Modern Sexual Assault Myth Acceptance Scale (AMMSA), where its expressions are modern, subtle and indirect. Considering the wide use of these instruments, it is justified to provide empirical evidence showing information on the psychometric properties of these scales. One of the procedures for synthesizing empirical results is meta-analyses (MA). This methodology can synthesize studies of specific variables and analyze the psychometric properties of the measurement instruments, providing relevant information on the quality of a given scale. Within this last type of RM are reliability generalizations (RG), those that study the reliability coefficients obtained in different applications of a scale, providing evidence on the properties of the measures used in measuring a construct. A meta-analysis of the RGs of 69 empirical studies that used any of the rape myth scales was performed. The objective was to estimate the mean reliability of the combined scores of the IRMAS and AMMSA scales to obtain an approximate value of their overall reliability and to assess the possible moderating effect of some variables of interest (e.g., research design, culture, sample type, etc.). The mean internal consistency reliability of the scale scores for the 98 samples studied was .85, 95 % C.I. [.84, .86] and the mean coefficient for each of the IRMAS and AMMSA scales was .84 and .85 respectively. All these values are above .80, a value established as satisfactory reliability of the instrument for general research. The Cronbach's alpha coefficients reported by the studies ranged from .71 to .98, with values considered moderate to excellent. These results show that both scales present acceptable internal consistency indices in various applications. There is high heterogeneity (I. = 96 %), with the number of items being the only moderating variable significantly explaining the observed reliability variability. This result was to be expected, given that the effect of test length on the estimation of reliability indices has a long tradition and is widely known in the psychometric literature.

Emotion regulation strategies and the two-dimensional model of adult attachment: a pilot study.

Introduction: Emotion Regulation plays a crucial role in human's daily lives. Extensive research has shown that people with different attachment orientations exhibit divergencies in how they perform emotion regulation strategies. Methods: 44 adults performed an experimental emotion regulation task in which they were instructed to attend, reappraise, or suppress their emotions while viewing negative and neutral images taken from the International Affective Picture System (IAPS). Afterward, participants rated valence, arousal, and emotional dominance elicited by the images. Additionally, attachment orientations were measured using the ECR-12 questionnaire. Results: Results showed a relationship between attachment avoidance and the level of arousal during the reappraisal condition; specifically, the higher attachment avoidance levels, the greater the emotional intensity during the implementation of cognitive reappraisal strategy. Such results suggest an association between failing in downregulate intense emotions using cognitive reappraisal when there are higher levels of attachment avoidance. Consistently, we also found that lower dominance during reappraisal was associated with more levels of avoidance. Conclusion: These results indicate that people with higher levels of attachment avoidance experience difficulties when using the cognitive reappraisal strategy to reduce the emotional impact produced by negative emotional stimuli. Our findings reinforce the idea that avoidant people experience high physiological activation when experience emotions.

Nutritional lipidomics for the characterization of lipids in food.

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

Health data hubs: an analysis of existing data governance features for research.

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.

Desiderata for the Data Governance and FAIR Principles Adoption in Health Data Hubs.

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.

From FAIR4Health Project to 1+MG Initiative: A Spain - Italy Collaboration.

Results of two major projects funded by the European Union are taken into consideration: Fair4Health regarding the possibility of sharing clinical data in various environments applying FAIR principles and 1+Million Genome for the in-depth study of the human genome in Europe. Specifically, the Gaslini hospital plans to move on both areas joining the Hospital on FHIR initiative matured within the fair4health project and also collaborate with other Italian healthcare facilities through the implementation of a Proof of Concept (PoC) in the 1+MG. The aim of this short paper is to evaluate the applicability of some of the tools of the fair4health project to the Gaslini infrastructure to facilitate its participation in the PoC. One of the aims is also to prove the possibility of reuse the results of well-performed European funded projects to boost routine research in qualified healthcare facilities.

Mobile Health Requirements for the Occupational Health Assessment of Health Care Professionals: Delphi Study.

BACKGROUND: In recent years, owing to the COVID-19 pandemic, awareness of the high level of stress among health care professionals has increased, and research in this area has intensified. Hospital staff members have historically been known to work in an environment involving high emotional demands, time pressure, and workload. Furthermore, the pandemic has increased the strain experienced by health care professionals owing to the high number of people they need to manage and, on many occasions, the limited available resources with which they must carry out their functions. These psychosocial risks are not always well dealt with by the organization or the professionals themselves. Therefore, it is necessary to have tools to assess these psychosocial risks and to optimize the management of this demand from health care professionals. Digital health, and more specifically, mobile health (mHealth), is presented as a health care modality that can contribute greatly to respond to these unmet needs. OBJECTIVE: We aimed to analyze whether mHealth tools can provide value for the study and management of psychosocial risks in health care professionals, and assess the requirements of these tools. METHODS: A Delphi study was carried out to determine the opinions of experts on the relevance of using mHealth tools to evaluate physiological indicators and psychosocial factors in order to assess occupational health, and specifically, stress and burnout, in health care professionals. The study included 58 experts with knowledge and experience in occupational risk prevention, psychosocial work, and health-related technology, as well as health professionals from private and public sectors. RESULTS: Our data suggested that there is still controversy about the roles that organizations play in occupational risk prevention in general and psychosocial risks in particular. An adequate assessment of the stress levels and psychosocial factors can help improve employees' well-being. Moreover, making occupational health evaluations available to the team would positively affect employees by increasing their feelings of being taken into account by the organization. This assessment can be improved with mHealth tools that identify and quickly highlight the difficulties or problems that occur among staff and work teams. However, to achieve good adherence and participation in occupational health and safety evaluations, experts consider that it is essential to ensure the privacy of professionals and to develop feelings of being supported by their supervisors. CONCLUSIONS: For years, mHealth has been used mainly to propose intervention programs to improve occupational health. Our research highlights the usefulness of these tools for evaluating psychosocial risks in a preliminary and essential phase of approaches to improve the health and well-being of professionals in health care settings. The most urgent requirements these tools must meet are those aimed at protecting the confidentiality and privacy of measurements.

FAIR principles to improve the impact on health research management outcomes.

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.

Propuesta de Valores de Referencia para el Cuestionario de Evaluación del Apego Adulto: Experiences in Close Relationships (ECR-12), en población adulta chilena / Reference values for the Adult Attachment Assessment Questionnaire: Experiences in Close Relationships (ECR-12) in the Chilean Adult Population

El cuestionario de Experiencias en Relaciones Cercanas (ECR) es un instrumento de auto-reporte ampliamente utilizado para evaluar el apego en la adultez, a partir de dos dimensiones: la ansiedad y la evitación asociadas al apego. Este instrumento ha sido adaptado en múltiples contextos, incluyendo el chileno, del cual existe una versión abreviada (ECR-12), objeto de análisis del presente estudio. Si bien existe consenso en que la seguridad en el apego es mejor descrita en términos dimensionales, hay ámbitos, como en la práctica clínica donde contar con valores de referencia podría ser de utilidad. El objetivo de este estudio es proveer valores de referencia para la interpretación de los valores del ECR-12 en el contexto chileno. Para ello, una muestra de 6779 participantes respondió el ECR-12. Se utilizó el método de puntuación z con normalización para obtener los valores de referencia. Los análisis realizados evidenciaron la necesidad de construir baremos diferenciados por edad. Así, para el grupo de 29 años o menos, el punto de corte en la dimensión de ansiedad es de un promedio igual o superior a 4.4 puntos, y para la evitación, el punto de corte es de un promedio igual o superior a 2.5. En cambio, para el grupo de 30 años o más, el punto de corte en la dimensión de ansiedad es de un promedio igual o superior a 4.2, y para la evitación, es de un promedio igual o superior a 2.9. Estos hallazgos pueden ser relevantes no sólo para identificar a personas que puedan presentar niveles sustancialmente altos de ansiedad y/o evitación en el apego, sino, también puede constituirse como una herramienta clínica complementaria en contextos terapéuticos.

The Experiences in Close Relationships (ECR) questionnaire is a widely used self-report measure to assess adult attachment, based on two dimensions: attachment anxiety and attachment avoidance. This instrument has been adapted in multiple contexts, including the Chilean, for which there is an abbreviated version (ECR-12), that is the object of analysis in the present study. Although there is consensus that attachment security is best described in dimensional terms, there are areas, such as clinical practice, where having reference values could be useful. The aim of this study is to provide reference values for the interpretation of ECR-12 scores in the Chilean context. To do this, a sample of 6779 participants was evaluated using the ECR-12. The z-score normalization method was used to obtain the reference values. The analyzes carried out showed the need to build scales differentiated by age. Thus, for the group of 29 years or less, the cut-off point in the anxiety dimension is an average equal to or greater than 4.4 points, and for avoidance, the cut-off point is an average equal to or greater than 2.5. On the other hand, for the group aged 30 or older, the cut-off point in the anxiety dimension is an average equal to or greater than 4.2, and for avoidance, it is an average equal to or greater than 2.9. These findings can be relevant not only for identifying people who may present substantially high levels of anxiety and/or avoidance in attachment, but also as a complementary clinical tool in therapeutic contexts.

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.

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.

Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

[This corrects the article DOI: 10.2196/27990.].

Modern Rape Myths: Justifying Victim and Perpetrator Blame in Sexual Violence.

Rape myths are beliefs, stereotypes, and attitudes usually false, widespread, and persistent about rape, victims, and perpetrators. They aim to deny and justify men's sexual assault against women. This study evaluates the mediating effect of modern rape myths on the relationship between gender system justification and attribution of blame to both victim and perpetrator in a fictional case of sexual violence. A total of 375 individuals residing in Chile, 255 women and 120 men, 19-81 years (M = 37.6 SD = 13.06) participated in the study. Results from a Structural Equation Model show that gender system justification is directly related to the attribution of blame to the victim, showing an indirect relationship throughout the modern rape myth. However, gender system justification and attribution of blame to the aggressor are indirectly related, being mediated by modern rape myths. The study of the relationship between the acceptance of modern rape myths, gender-specific system justification, and victim and aggressor blame for rape is a contribution to understanding beliefs justifying sexual violence against women.

Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population.

Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke's Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.

Safety of Stromal Vascular Fraction Cell Therapy for Chronic Kidney Disease of Unknown Cause (Mesoamerican Nephropathy).

Chronic kidney disease of unknown cause (CKDu), also known as Mesoamerican nephropathy, typically presents as an ischemic nephropathy with chronic tubulointerstitial fibrosis in normotensive patients, rapidly progressing to kidney failure. In this first-in-human, open-label, safety study, we followed 18 patients with CKDu (stages 3-5) for 36 months after receiving a single infusion of angiogenic/anti-fibrotic autologous adipose-derived stromal vascular fraction (SVF) cells into their kidneys bilaterally via renal artery catheterization. SVF therapy was safe and well tolerated. There were no SVF-related serious adverse events and no procedural complications. Color Doppler evaluation at 2 months demonstrated increased perfusion to the interlobar and/or arcuate artery levels in each kidney evaluated (36/36) with a reduction in resistance index at the hilar artery (35/36) kidneys. Beyond 12 months, patients with initial eGFR <30 mL/minute/1.73 m2 deteriorated, whereas those ≥30 mL/minute/1.73 m2 further sustained their renal function, suggesting a possible renal protective effect in that group.