Common Data Research Articles

How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned

BackgroundGiven the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.MethodsIn an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM.ResultsWe have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs.DiscussionThis work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM.ConclusionThe customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Assessing Completeness of IoT Data: A Novel Probabilistic Approach

AbstractThe Internet of Things (IoT) is one of the driving forces behind Industry 4.0 and has the potential to improve the entire value chain, especially in the context of industrial manufacturing. However, results derived from IoT data are only viable if a high level of data quality is maintained. Thereby, completeness is especially critical, as incomplete data is one of the most common and costly data quality defects in the IoT context. Nevertheless, existing approaches for assessing the completeness of IoT data are limited in their applicability because they assume a known number of real-world entities or that the real-world entities appear in regular patterns. Thus, they cannot handle the uncertainty regarding the number of real-world entities typically present in the IoT context. Against this background, the paper proposes a novel, probability-based metric that addresses these issues and provides interpretable metric values representing the probability that an IoT database is complete. This probability is assessed based on the detection of outliers regarding the deviation between the estimated number of real-world entities and the number of digital entities. The evaluation with IoT data from a German car manufacturer demonstrates that the provided metric values are useful and informative and can discriminate well between complete and incomplete IoT data. The metric has the potential to reduce the cost, time, and effort associated with incomplete IoT data, providing tangible benefits in real-world applications.

BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis.

Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.

Collaborative data sharing: The International Acute Myeloid Leukemia Consortium (INTERACT).

169 Background: Outcomes for children and adolescents/young adults (AYAs) with acute myeloid leukemia (AML) remain suboptimal and are decidedly poor for patients with subtypes defined by high-risk features, including canonical genetic alterations and relapsed/refractory presentation. Due to their rarity, these subtypes are difficult to study within the existing clinical trials paradigm. Data sharing efforts aim to increase our understanding of rare cancer subtypes by pooling data from multiple sources. The INTERnational Acute myeloid leukemia ConsorTium (INTERACT) was established to pool and share data on pediatric and AYA patients with AML. Methods: In 2019, a data dictionary was created through an iterative, consensus-driven process that analyzed pre-existing clinical trials case report forms as the initial basis. By developing this standardized representation for data on patients with AML, data could be harmonized and integrated. A memorandum of understanding was signed in 2021, formally establishing INTERACT. An executive committee was convened to establish scientific priorities, facilitate ongoing data dictionary work, usher agreements with data contributors and review data requests submitted by investigators. In January 2024, the initial set of AML data was released on the Pediatric Cancer Data Commons (PCDC) data portal. Results: As of April 2024, the data dictionary includes 157 standardized data elements that were used to harmonize clinical data on 3,413 patients who participated in seven clinical trials conducted by four pediatric oncology cooperative groups across the United States, Europe, and Asia. Elements in the data dictionary include demographics, initial disease characteristics, genetic alterations, and survival status. Aggregate data can be freely explored using the publicly-available PCDC data portal (portal.pedscommons.org) and investigators seeking line-level access to data may submit a project request to the INTERACT executive committee for consideration. Conclusions: International collaborative data-sharing efforts will advance our understanding of pediatric and AYA AML by reducing barriers that constrain our study of AML to smaller cohorts of data. We hope that an early outcome of data sharing will be to increase our understanding of optimal treatments for patients with relapsed/refractory disease. Future goals of INTERACT include identifying data from additional studies to include, establishing relationships with new data contributors, and establishing working groups to advance the consortium’s scientific and clinical trials goals.

Exercise and nutrition to improve cancer Treatment-Related outcomes (ENICTO).

Chemotherapy treatment-related side-effects are common and increase the risk of suboptimal outcomes. Exercise interventions during cancer treatment improve self-reported physical functioning, fatigue, anxiety, and depression, but it is unclear whether these interventions improve important clinical outcomes, such as chemotherapy relative dose intensity (RDI). The National Cancer Institute funded the Exercise and Nutrition to Improve Cancer Treatment-Related Outcomes (ENICTO) Consortium, to address this knowledge gap. This paper describes the mechanisms hypothesized to underpin intervention effects on clinically-relevant treatment outcomes, briefly outlines each project's distinct research aims, summarizes the scope and organizational structure of ENICTO, and provides an overview of the integrated common data elements used to pursue research questions collectively. In addition, the paper includes a description of consortium-wide activities and broader research community opportunities for collaborative research. Findings from the ENICTO Consortium have the potential to accelerate a paradigm shift in oncology care such that cancer patients could receive exercise and nutrition programming as the standard of care in tandem with chemotherapy to improve RDI for a curative outcome.

ENCoDE - a skin tone and clinical dataset from a prospective trial on acute care patients.

Although hypothesized to be the root cause of the pulse oximetry disparities, skin tone and its use for improving medical therapies have yet to be extensively studied. Studies previously used self-reported race as a proxy variable for skin tone. However, this approach cannot account for skin tone variability within race groups and also risks the potential to be confounded by other non-biological factors when modeling data. Therefore, to better evaluate health disparities associated with pulse oximetry, this study aimed to create a unique baseline dataset that included skin tone and electronic health record (EHR) data. Patients admitted to Duke University Hospital were eligible if they had at least one pulse oximetry value recorded within 5 minutes before an arterial blood gas (ABG) value. We collected skin tone data at 16 different body locations using multiple devices, including administered visual scales, colorimetric, spectrophotometric, and photography via mobile phone cameras. All patients' data were linked in Duke's Protected Analytics Computational Environment (PACE), converted into a common data model, and then de-identified before publication in PhysioNet. Skin tone data were collected from 128 patients. We assessed 167 features per skin location on each patient. We also collected over 2000 images from mobile phones measured in the same controlled environment. Skin tone data are linked with patients' EHR data, such as laboratory data, vital sign recordings, and demographic information. Measuring different aspects of skin tone for each of the sixteen body locations and linking them with patients' EHR data could assist in the development of a more equitable AI model to combat disparities in healthcare associated with skin tone. A common data model format enables easy data federation with similar data from other sources, facilitating multicenter research on skin tone in healthcare. A prospectively collected EHR-linked skin tone measurements database in a common data model with emphasis on pulse oximetry disparities.

Bridging Data Standards and FAIR Principles in Biodiversity Digital Twinning: Prototyping, Challenges, Lessons Learned, and Future Plans

Digital twins combine modelling, domain knowledge, computing power, and multiple datasets to offer the potential to unlock new insights into biodiversity (de Koning et al. 2023). The Biodiversity Digital Twin (BioDT) project pioneers this approach to aid in understanding biodiversity through prototyping digital twins (Golivets et al. 2024). However, working with biodiversity data presents challenges due to their dynamic and diverse nature as well as the need for having to deal with incompleteness, uncertainties (Rocchini et al. 2011), disproportionate representation patterns in global studies from wealthier economies (Hughes et al. 2024), and issues with data aggregation and integration (Wüest et al. 2020). Similar to BioDT, there are also plans for creating a Digital Twin of the Ocean (DTO). DTO-Bioflow project addresses these data challenges in the marine domain, where studies show that although European seas host 48,000 marine species (75% described), the data are not yet FAIR (Findable, Accessible, Interoperable, and Reusable) (Ramírez et al. 2022). These challenges hamper the modelling capabilities needed for effective predictions and conservation prioritisation. The adaptability of digital twins across temporal and spatial scales and their ability to model dynamic ecosystems make them ideal for biodiversity research and real-time conservation efforts. However, their success hinges on the consistent integration and alignment of data from disparate sources (Trantas et al. 2023). This integration involves standardising terms used to describe datasets, such as temporal coverage or controlled vocabularies like "Forest" for targetHabitatScope. Thus, adopting data standards is essential. Additionally, challenges such as model bias (Lewers 2023), research context, and data provenance must be considered, adding complexity to metadata capture and alignment. BioDT addresses these challenges with modular building blocks for data integration, model deployment, and workflow management. This approach facilitates the gradual adoption of data standards and FAIR principles, which need to encompass not just data, but also models and software. As automation, ease of reproducibility, and deployability are critical for digital twinning success, data integration and interoperability issues may arise due to missing or insufficient parameter descriptions in the model, incomplete information on data selection, and the unavailability of required software package details. Thus, data standardisation provides a pathway for a consistent approach that can be adopted for different use cases. Common data sources in BioDT, like species occurrences and environmental variables, benefit from standards such as Darwin Core (Wieczorek et al. 2012) and the Ecological Metadata Language (Jones et al. 2019). While valuable, these standards may not fully encompass the complexity needed for comprehensive biodiversity digital twins. Additionally, differing familiarity with these standards and FAIR principles among communities pose challenges. Continuous adoption of data standards, alongside exploring complementary approaches like schema.org or bioschemas.org for capturing diverse (meta)data, is essential. Collaboration with data providers, modellers, and various research infrastructures is also crucial (Andrew et al. 2024). We share our experience using Research Object Crate (RO-Crate), leveraging common JavaScript Object Notation for Linked Data (JSON-LD) representation for metadata profiles and workflow representation, to connect with different infrastructures. In the BioDT project, we are working with various use cases to create prototype digital twins that can serve as valuable resources for other projects. The evolving landscape of digital twin concepts, along with other European Union-funded initiatives like DTO-Bioflow and Destination Earth (DestinE), emphasises the importance of alignment within the digital twin ecosystem. BioDT is committed to aligning with and contributing to this broader context, highlighting the critical role of data standardisation and FAIR implementation.

Common sleep data pipeline for combined data sets.

Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizing results. However, working with large amounts of sleep data can be challenging, both from a hardware perspective and because of the different preprocessing steps necessary for distinct data sources. Here we review the possible obstacles and present an open-source pipeline for automatic data loading. Our solution includes both a standardized data store as well as a 'data serving' portion which can be used to train neural networks on the standardized data, allowing for different configuration options for different studies and machine learning designs. The pipeline, including implementation, is made public to ensure better and more reproducible sleep research.

Co-design Pedagogy for Computational Thinking Education in K-12: A Systematic Literature Review

AbstractThe recent popularity of computational thinking (CT) and the desire to apply CT in our daily lives have prompted the need for a successful pedagogical technique for learning CT in K-12 education. The application of co-design pedagogical techniques has the potential to improve students’ CT learning through knowledge sharing and the creation of ideas to solve problems and develop an artifact. However, there is a limited understanding of how co-design pedagogical techniques have been explored to foster CT learning, which could hamper the successful use of co-design as a pragmatic teaching approach. This study examined the ways in which co-design pedagogical techniques have been applied in CT education by implementing a systematic literature review (PRISMA protocol) to document the review analysis. A total of 26 articles that met the inclusion criteria for this study were reviewed. Findings in this study revealed that workshops are the most utilized co-design learning setting and, as expected, the collaborative technique is the co-design pedagogical technique most frequently adopted for implementing CT in K-12 education. NetLogo is the most frequently used co-design tool for teaching and learning CT in K-12 education, and an interdependence exists between NetLogo and the Common Online Data Analysis Platform. Co-design also helps teachers develop the ability to use co-design pedagogical techniques to learn, create content, and integrate CT into their various subjects. This study contributes to practical knowledge by unraveling and advocating the use of dialogical, prompting, framing, and game-based techniques as co-design pedagogical techniques for K-12 teachers and also helps teachers identify useful co-design tools for learning CT.

Towards a minimum data set for the female stress urinary incontinence surgical literature: a collaborative work from Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction, American Urogynecologic Society, and International Continence Society.

There has been a need for an acceptable common minimum data set in the scientific literature as regards the surgical treatment of female stress urinary incontinence (SUI). Such a data set, if widely adopted, would improve the quality of the literature and allow objective comparisons between and across interventions. The surgical treatment of female stress urinary incontinence has evolved considerably over the past few decades. The corresponding body of literature has grown exponentially describing the outcomes of hundreds of studies of these novel interventions. However, historically, the literature in this space has been of uneven quality. In order to improve the reporting of clinical studies, and ultimately patient outcomes, a standard minimum data set for trial design and publications was created by a collaborative group formed from leading scientific societies. The consensus document created from this novel collaboration between members of SUFU (Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction), AUGS (American Urogynecologic Society), and ICS (International Continence Society) provides clear guidance for the structure of clinical studies and reporting of results in the peer-reviewed literature. This has substantial potential ramifications for scientific journals, journal editors, peer reviewers, investigators, regulatory agencies, industry, clinicians, and patients.

WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring

This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in the WATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A&amp;amp;B); 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A&amp;amp;B); 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data.

A Systematic Review of Stakeholders’ Engagement as a Critical Tool for Effective Project Performance

A comprehensive review of stakeholders’ engagement (SE) articles published in the last decade (2014-2023) was undertaken in this work. 100 SE publications were identified using the Systematic Quantitative Assessment Technique. Time distribution, geographic distribution, article type, data gathering methods, and themes discussed were all covered in the review. The data revealed that interest in SE has been at increase in the last decade, and that North America and Europe are the regions with the least amount of SE research. The ratio of conceptual to empirical SE articles was skewed toward the latter, and the most common data collection method was survey. The most striking finding was that, in order to ensure successful implementation and performance of projects, stakeholders’ engagement from conception to actual implementation and supervision of projects is not only desirable but necessary to ensure clear understanding, continuous, transparent, participatory, and more often than sustainable process.

Long-term impacts of COVID-19 in patients with prior heart failure in Korea: A nationwide cohort study using the common data model.

Limited data are available on the long-term prognosis and monitoring period after coronavirus disease 2019 (COVID-19) infection in the population with prior heart failure (HF). We aimed to exam the association of COVID-19 with clinical prognosis in populations with prior HF and evaluate prognosis within 30 days and 30 days to 1 year after infection. Based on insurance benefit claims sent to the Health Insurance Review and Assessment Service of Korea from January 2018 to April 2022, 9,822,577 patients were selected and converted to the Observational Medical Outcomes Partnership-common data model by the Big Data Department of Health Insurance Review and Assessment Service of Korea. In the dataset, 1,565,274 patients exhibited diagnosis of HF based on the International Statistical Classification of Diseases and Related Health Problems 10 codes. They were divided into 2 groups according to COVID-19 infection, and propensity-score-matching analysis was performed. The clinical outcome was all-cause mortality. Among the 1,565,274 patients with an HF diagnosis, 1,152,975 patients were classified into the HF with the COVID-19 group and 412,299 patients in the HF without COVID-19 group. We created 200,780 matched pairs by propensity-score-matching analysis. Within 30 days of COVID-19, the HF with COVID-19 group had a higher risk of all-cause death compared with the HF without COVID-19 group (hazard ratio [HR]: 2.19, 95% confidence interval [CI]: 2.04-2.36, P < .01). Thirty days to 1 year after COVID-19 infection, the HF with COVID-19 group exhibited a higher risk of all-cause death (HR: 2.04, 95% CI: 1.83-2.27, P < .01). In populations with prior HF, COVID-19 is associated with a higher risk of all-cause mortality within 30 days and this risk remains augmented up to 1 year after the acute phase of COVID-19. Our findings suggest that greater attention may be crucial in populations with prior HF for a prolonged period after COVID-19 infection.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program.

The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework. We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older. Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality. This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

Benefits and Challenges of Implementing the Common Data Environments in the Construction Phase of BIM-Based Projects

Benefits and Challenges of Implementing the Common Data Environments in the Construction Phase of BIM-Based Projects

One time pad for enhanced steganographic security using least significant bit with spiral pattern

Data is an important commodity in today’s digital era. Therefore, data needs to get adequate security to prevent misuse. A common data security practice in the transmission of information is cryptography. Another approach is steganography, which hides secret messages in other media that are not confidential and can be accessed by the public. In this study, the spiral pattern is used for data placement using the least significant bit (LSB) method. Modifications were made to the 2-bits LSB to increase the data capacity that can be hidden. In order to increase security, the data is first converted into a datastream using random numbers as one time pad (OTP). exclusive-OR (XOR) operation is performed on datastream and OTP to get encrypted data to be hidden. The results showed that the image quality of the steganography results at a capacity close to 100% was still fairly good, as indicated by a peak signal-to-noise ratio (PSNR) value greater than 46 dB. Visually, the steganographic image does not look different from the original one. Likewise, the use of random numbers as OTP succeeded in changing the hidden data significantly, as indicated by the avalanche effect value above 50%.

Europe's 21st century performance and its engagement with postmodern culture: difference and event

Introduction: This paper, from a poststructuralist approach of event and, besides, of difference (keys transversal to postmodernity and affecting the performative phenomena), intends to set up a theoretical framework out of the "problematic", from the question and the plight, rather than on the basis of the so-called apodictic, out of what is necessarily valid. This model to which we refer, made up of several philosophical concepts from current thought, tries to provide some keys from which to catch the so-called "performance" in the European stage of the 21st century. Methodology: An analysis of the performances in their constituent parts is carried out, and then the common data are synthesized in order to draw relevant conclusions. Results and Conclusions: The philosophical-performative model, compared and analyzed with the performances of representative groups on the European continent, is validated. Discussion: While interpreting the results and bringing them into contact with our transdisciplinary theoretical-factual corpus (the fruit of philosophical-dramatic research, of conversations and experiences in the field of dramaturgy and also of the stage), we observe not only that the variables are validated and meet the criteria of performativity, but also that it is not possible to understand this phenomenon apart from its postmodern grassroots.

Clinical outcomes of COVID-19 infection in patients with osteoporosis: a nationwide cohort study in Korea using the common data model

Many older patients with COVID-19 likely have co-morbid osteoporosis. We investigated the clinical outcomes of COVID-19 patients with osteoporosis. This was a retrospective cohort study using national claims data from Korea encoded in the common data model. Patients aged ≥ 50 years diagnosed with COVID-19 infection between January 2020 and April 2022 were included and stratified into two groups according to a history of osteoporosis. Clinical outcomes of COVID-19 infection were analyzed using logistic regression analysis after large-scale propensity score stratification. Of the 597,011 patients with COVID-19 included in the study, 105,172 had a history of osteoporosis. In patients with a history of osteoporosis, the odds of mortality decreased (odds ratio [OR] 0.82, P < 0.002), whereas most clinical outcomes of COVID-19 did not exhibit differences compared to those without such a history. Osteoporosis patients with a history of fractures showed increased odds of pneumonia, hospitalization, major adverse cardiac events, venous thromboembolism, and mortality, compared to patients without osteoporosis (ORs 1.34–1.58, P < 0.001 to P = 0.001). Our study suggests that patients with severe osteoporosis who have experienced fractures have an elevated risk of severe complications with COVID-19, while osteoporosis patients without fractures who have sought medical attention have a lower risk of mortality.