Interoperable Datasets Research Articles

Using interoperable nursing care data to improve outcomes for multiple traumas patients with Impaired Physical Mobility.

To demonstrate how interoperable nursing care data can be used by nurses to create a more holistic understanding of the healthcare needs of multiple traumas patients with Impaired Physical Mobility. By proposing and validating linkages for the nursing diagnosis of Impaired Physical Mobility in multiple trauma patients by mapping to the Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC) equivalent terms using free-text nursing documentation. A descriptive cross-sectional design, combining quantitative analysis of interoperable data sets and the Kappa's coefficient score with qualitative insights from cross-mapping methodology and nursing professionals' consensus. Cross-mapping methodology was conducted in a Brazilian Level 1 Trauma Center using de-identified records of adult patients with a confirmed medical diagnosis of multiple traumas and Impaired Physical Mobility (a nursing diagnosis). The hospital nursing free-text records were mapped to NANDA-I, NIC, NOC and NNN linkages were identified. The data records were retrieved for admissions from September to October 2020 and involved medical and nursing records. Three expert nurses evaluated the cross-mapping and linkage results using a 4-point Likert-type scale and Kappa's coefficient. The de-identified records of 44 patients were evaluated and then were mapped to three NOCs related to nurses care planning: (0001) Endurance; (0204) Immobility Consequences: Physiological, and (0208) Mobility and 13 interventions and 32 interrelated activities: (6486) Environmental Management: Safety; (0840) Positioning; (3200) Aspiration Precautions; (1400) Pain Management; (0940) Traction/Immobilization Care; (3540) Pressure Ulcer Prevention; (3584) Skincare: Topical Treatment; (1100) Nutrition Management; (3660) Wound Care; (1804) Self-Care Assistance: Toileting; (1801) Self-Care Assistance: Bathing/Hygiene; (4130) Fluid Monitoring; and (4200) Intravenous Therapy. The final version of the constructed NNN Linkages identified 37 NOCs and 41 NICs. These valid NNN linkages for patients with multiple traumas can serve as a valuable resource that enables nurses, who face multiple time constraints, to make informed decisions efficiently. This approach of using evidence-based linkages like the one developed in this research holds high potential for improving patient's safety and outcomes. In this study, there was no direct involvement of patients, service users, caregivers or public members in the design, conduct, analysis and interpretation of data or preparation of the manuscript. The study focused solely on analysing existing de-identified medical and nursing records to propose and validate linkages for nursing diagnoses.

SMART-FCD: IOT DATA INTEROPERABILITY USING SENSOR BASED FUZZY LINKED RULES FOR CROSS DOMAIN APPLICATIONS

Internet of Things (IoT) is connected everywhere and enables massive information exchange between objects and people. The unified global IoT must manage a massive amount of data generated by these connected smart which arises the interoperability challenges such as lack of communication protocols, device support, and accepted open standards in smart environments. To combat these issues, a novel Smart-Fuzzy linked rules for Cross Domain application (Smart-FCD) framework is proposed to ensure interoperability by enabling efficient communication and data exchange between multiple platforms and systems. Initially, the heterogenous data from the sensors such as temperature sensors and humidity sensors, and the descriptions are implemented according to the Sensor Measurement List (SenML) language. After composing, semantic modelling will occur which converts the relational data into Resource Description Framework (RDF) format. The Linked Open Vocabulary for the Internet of Things (LOV4IoT) dataset is used to extract interoperable domain knowledge, which is then fed into the related sensor-based fuzzy rules. The IoT preset semantic models which utilise interoperable datasets and domain instances, which are updated via fuzzy association rules. Semantic Web of Things (SWoT) is a technology that assists in creating semantic-based IoT applications, thus IoT developers may use it to create intelligent applications. The sensor data, RDF simulation, evolution time and latency are some of the parameters that are used to evaluate the effectiveness of the proposed Smart-FCD methodology.

MMLKG: Knowledge Graph for Mathematical Definitions, Statements and Proofs

Nowadays, Knowledge Graphs (KGs) are important and developing in different areas. However, there is a lack of genuinely interoperable datasets representing mathematics that allow for information exchange between datasets in the Web ecosystem. In this paper, we address this matter based on the Mizar Mathematical Library (MML), a collection of articles written in the Mizar language. MML includes definitions and theorems with proofs to which authors can easily refer from newly written Mizar articles. However, extracting information directly from Mizar scripts by external projects is not very straightforward. Therefore, we propose a new data storage and retrieval approach based on the Knowledge Organization System (KOS) model and the KG concept that provides a way to organize and access knowledge. We present Mizar Mathematical Library Knowledge Graph (MMLKG), a thesaurus for describing mathematical objects. MMLKG supports semantic interoperability and allows linking data from different sources, e.g., Wikidata. Moreover, it satisfies the FAIR data principles. The data is publicly available via a Cypher endpoint.

Creating Data Standards to Support the Electronic Transmission of Compounded Nonsterile Preparations (CNSPs): Perspectives of a United States Pharmacopeia Expert Panel.

The perspectives of the Compounded Drug Preparation Information Exchange Expert Panel of the United States Pharmacopeia (CDPIE-EP) on the urgent need to create and maintain data standards to support the electronic transmission of an interoperable dataset for compounded nonsterile preparations (CNSPs) for children and the elderly is presented. The CDPIE-EP encourages all stakeholders associated with the generation, transmission, and preparation of CNSPs, including standards-setting and informatics organizations, to discern the critical importance of accurate transmission of prescription to dispensing the final product and an urgent need to create and adopt a seamless, transparent, interoperable, digitally integrated prescribing and dispensing system benefiting of all patients that need CNSPs, especially for children with special healthcare needs and medical complexity (CSHCN-CMC) and for adults with swallowing difficulties. Lay summary: Current electronic prescription processing standards do not permit the complete transmission of compounded nonsterile preparations (CNSPs) from a prescriber to dispenser. This lack creates multiple opportunities for medication errors, especially at transitions of care for children with medical complexity and adults that cannot swallow tablets and capsules. The United States Pharmacopeia Expert Panel on Compounded Drug Preparation Information Exchange aims to reduce this source of error by creating ways and means for CNSPs to be transmitted within computer systems across the continuum of care. Twitter: Digitizing compounded preparation monographs and NDC-like formulation identifiers in computerized prescription systems will minimize error.

QMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

Increasing the interoperability of snow/ice hyperspectral observations

This study aims to set up a metadata profile useful for preparing an interoperable dataset containing snow and ice hyperspectral measurements. The proposed Snow and Ice Spectral Library (SISpec) scheme was prepared for sharing a data collection focused on Antarctica, including 70 observations. Following the perspective to grant “open access” to such a dataset, we found a compromise between the ERC (European Research Council) guidelines, the FAIR (Findability, Accessibility, Interoperability, and Reuse) Data principles defined by the RDA (Research Data Alliance), and the GEO (Group on Earth Observation) Data Sharing Principles. The ISO (International Organization for Standardization) standard 19115 was chosen as the standard framework for describing SISpec. When the available metadata scheme was not sufficient or suitable, metadata extensions or new detailed metadata components were created to be compliant with the ISO 19115 standard. We also considered the INSPIRE (Infrastructure for Spatial Information in Europe) requirements and the result is a metadata model that can be useful to share SISpec metadata both in the European and international contexts. Particularly detailed metadata sections and elements were created for describing spectral signatures and microphysical snow parameters.

Designing a standardized framework for data integration between zoonotic diseases systems: Towards one health surveillance

BackgroundZoonotic diseases or zoonoses account for a considerable ratio of infectious diseases outbreak; their effective surveillance demands coordinated actions by human and animal health organizations. However, zoonoses surveillance data are collected individually from standalone information systems for either humans or animals with varied structures, processes and applications. In moving towards one health (OH) surveillance, integrating the zoonoses data may help prevent and control these diseases. Therefore, this research aimed to determine essential data elements and a consistent reporting template towards interoperability. Material and methodsIn this study, first, the zoonotic diseases minimum dataset (ZD-MDS) was identified according to a comprehensive literature review coupled with the agreements of experts. Then, the ZD-MDS was mapped to structured clinical vocabularies. Also, the health level seven-clinical document architecture (HL7-CDA) standard was used to define the interoperable and human-machine reporting template. ResultsThe ZD-MDS was divided into administrative and clinical sections with five and seven data classes and total of 38 and 57 data elements, respectively. Then, the corresponding data values and systematized nomenclature of medicine-clinical terms (SNOMED-CT) codes were defined for each data element. The reporting template was structured according to three sections of CDA template, extensible markup language (XML) hierarchical and tags. ConclusionOur study suggested that zoonoses surveillance could be improved by integrating and exchanging data from different databases across humans and animal organizations. The developed template provided a comprehensive and interoperable dataset, making data more comparable and reportable across multiple studies and settings.

Open the doors to (de-identifiable) data.

Open the doors to (de-identifiable) data.

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions.

The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease. Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic.

BACKGROUND:Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.MATERIALS AND METHODS:First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.RESULTS:A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.CONCLUSION:The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond

BackgroundThe current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.MethodsBased on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.ResultsA core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.ConclusionGECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Efficient, semantics-rich transformation and integration of large datasets

The digital age is making more datasets available through the Internet, but their interoperability is still limited. The Semantic Web should play a fundamental role in achieving interoperable datasets. The semantic exploitation of data requires its efficient transformation into semantic formats and the integration of heterogeneous sources. Either the scalability of the existing tools for the semantic transformation of large volumes of data is limited or these tools do not provide a semantics-rich representation of the data.The goal of this work was to show how scalable semantic data transformation processes can be designed and implemented, thereby addressing the first limitation mentioned above. Here, we propose an application of high-performance computing techniques to overcome the scalability limitation. The proposed method was implemented as an upgrade of our Semantic Web Integration Tool (SWIT). Additional improvements for supporting the transformation process in SWIT are also described in this paper. We evaluated the new method by using three case studies from the areas of bioinformatics, movies and persons. The results showed a significant speed-up with respect to the original SWIT algorithm and the related tools. The lessons learnt in our work allowed us to configure semantic transformation processes efficiently.

Interoperable and accessible census and survey data from IPUMS

The first version of the Integrated Public Use Microdata Series (IPUMS) was released to users in 1993, and since that time IPUMS has come to stand for interoperable and accessible census and survey data. Initially created to harmonize U.S. census microdata over time, IPUMS now includes microdata from the U.S. and international censuses and from surveys on health, employment, and other topics. IPUMS also provides geo-spatial data, aggregate population data, and environmental data. IPUMS supports ten data products, each disseminating an integrated data collection with a set of tools that make complex data easy to find, access, and use. Key features are record-level integration to create interoperable datasets, user-friendly interfaces, and comprehensive metadata and documentation. The IPUMS philosophy aligns closely with the FAIR principles of findability, accessibility, interoperability, and re-usability. IPUMS data have catalyzed knowledge generation across a wide range of social science and other disciplines, as evidenced by the large volume of publications and other products created by the vast IPUMS user community.

Harmonization of Transport Data Sources According to INSPIRE Data Specification on Transport Networks

There are numerous sources of data referred to transport from different suppliers, the main one being the Public Administration. According to the objective of each project, datasets are collected and formatted in a particular way. Each public organization only covers the area that corresponds to it (municipal, provincial, regional, national), and this results in the network being covered in a complementary manner, but with no interoperable datasets formats. Conduct studies and projects whose area of study exceeds more than one scope involve making a first phase of harmonization of data with high costs. These costs could be reduced by using standard formats and interoperable data sets and services. The European Union has stimulated the process of standardization and interoperability through INSPIRE Directive (2007/2/EC), which will be fully implemented in 2019 in the Member States. In this paper a harmonized data model of existing datasets provided by the traffic detectors in urban areas of Seville and Malaga is proposed, because they are used in many traffic projects and studies. The main objective is subsequent adaptation to the proposal by the INSPIRE directive specification, taking into account compatibility with the standard format DATEX II. This process can help to accelerate the transition to the standardized format to which the European Union is directing transport networks, and data sets associated with them.

Creation of seamless geological map of Japan at the scale of 1:200,000 and its distribution through the web

A seamless geological map with unified legend was created at the scale of 1:200,000 covering the entire Japanese Archipelago. This was accomplished by harmonizing regional geological maps that were compiled in various ages. A research scenario from basic geological research to information technology (IT) for distribution of digital geological maps was designed. Type 2 Basic Research was proposed to distribute the interoperable data set of geological maps, which were transformed from the results of Type 1 Basic Research for geological mapping. Seamless geological mapping was finally proposed as a Full Research program in geological research field.