Comprehensive Data Model Research Articles

Improving radiology information systems for inclusivity of transgender and gender-diverse patients: what are the problems and what are the solutions? A systematic review.

In medical radiation science (MRS), radiology information systems (RISs) record patient information such as name, gender and birthdate. The purpose of RISs is to ensure the safety and well-being of patients by recording patient data accurately. However, not all RISs appropriately capture gender, sex or other related information of transgender and gender-diverse (TGD) patients, resulting in non-inclusive and discriminatory care. This review synthesises the research surrounding the limitations of RISs preventing inclusivity and the features required to support inclusivity and improve health outcomes. Studies were retrieved from three electronic databases (Scopus, PubMed and Embase). A quality assessment was performed using the Johns Hopkins Nursing Evidence-Based Practice Research and Non-Research Evidence Appraisal Tools. A thematic analysis approach was used to synthesise the included articles. Eighteen articles were included based on the predetermined eligibility criteria. The pool of studies included in this review comprised primarily of non-research evidence and reflected the infancy of this research field and the need for further empirical evidence. The key findings of this review emphasise how current systems do not record the patient's name and pronouns appropriately, conflate sex and gender and treat sex and gender as a binary concept. For current systems to facilitate inclusivity, they must implement more comprehensive information and data models incorporating sex and gender and be more flexible to accommodate the transient and fluid nature of gender. However, implementation of these recommendations is not without challenges. Additionally, further research focused on RISs is required to address the unique challenges MRS settings present to TGD patients.

Hebrew Manuscripts as a Source of Knowledge

Abstract The Hebrew Manuscripts Catalogue of the National Library of Israel holds a wealth of information about Jewish history, literature, and culture. However, its complex structure and limited accessibility have resulted in underutilized data. This study aims to harness new digital approaches to unlock the potential of this valuable resource. Specifically, we focus on 275 Yemenite Jewish manuscripts related to ‘sciences’ to explore complex relationships within the data. We began by consolidating metadata from the Hebrew Manuscripts Catalogue, often found scattered within the ‘notes’ field of the records. We supplemented this data with information from linked open data (LOD) entities as well as previous research. This comprehensive approach reveals details about copyists, authors, and owners, even identifying lesser-known figures. Our methodology involved using Nodegoat for data management and visualization. This platform allowed us to create a comprehensive data model for manuscript provenance and explore relationships between objects, transactions, persons, and organizations, along with geographical data.

Telehealth With Comprehensive Live-Fed Real-World Data as a Patient Care Platform for Lung Cancer: Implementation and Evaluation Study.

Telehealth has emerged as a popular channel for providing outpatient services in many countries. However, the majority of telehealth systems focus on operational functions and offer only a sectional patient journey at most. Experiences with incorporating longitudinal real-world medical record data into telehealth are valuable but have not been widely shared. The feasibility and usability of such a telehealth platform, with comprehensive, real-world data via a live feed, for cancer patient care are yet to be studied. The primary purpose of this study is to understand the feasibility and usability of cancer patient care using a telehealth platform with longitudinal, real-world data via a live feed as a supplement to hospital electronic medical record systems specifically from physician's perspective. A telehealth platform was constructed and launched for both physicians and patients. Real-world data were collected and curated using a comprehensive data model. Physician activities on the platform were recorded as system logs and analyzed. In February 2023, a survey was conducted among the platform's registered physicians to assess the specific areas of patient care and to quantify their before and after experiences, including the number of patients managed, time spent, dropout rate, visit rate, and follow-up data. Descriptive and inferential statistical analyses were performed on the data sets. Over a period of 15 months, 16,035 unique users (13,888 patients, 1539 friends and family members, and 174 physician groups with 608 individuals) registered on the platform. More than 382,000 messages including text, reminders, and pictures were generated by physicians when communicating with patients. The survey was completed by 78 group leaders (45% of the 174 physician groups). Of the participants, 84% (65.6/78; SD 8.7) reported a positive experience, with efficient communication, remote supervision, quicker response to questions, adverse event prevention, more complete follow-up data, patient risk reduction, cross-organization collaboration, and a reduction in in-person visits. The majority of the participants (59/78, 76% to 76/78, 97.4%) estimated improvements in time spent, number of patients managed, the drop-off rate, and access to medical history, with the average ranging from 57% to 105%. When compared with prior platforms, responses from physicians indicated better experiences in terms of time spent, the drop-off rate, and medical history, while the number of patients managed did not significantly change. This study suggests that a telehealth platform, equipped with comprehensive, real-world data via a live feed, is feasible and effective for cancer patient care. It enhances inpatient management by improving time efficiencies, reducing drop-off rates, and providing easy access to medical history. Moreover, it fosters a positive experience in physician-patient interactions.

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

Machine Learning-Based Email Spam Filter

The surge in email usage has been accompanied by an unwelcome increase in spam, posing challenges for individuals and organizations. This study addresses the urgent need for efficient spam detection by evaluating the performance of three widely-used machine learning classifiers: Naive Bayes, Random Forest, and Logistic Regression. Our approach includes comprehensive data preparation, feature extraction, model training, and rigorous performance evaluation using metrics such as Accuracy, Precision, Recall, and F1-Score. Furthermore, we analyze the trade-off between accuracy and computational efficiency, essential for real-time spam detection systems. A user-friendly interface developed with Flask showcases the practical application of our findings. The Random Forest Classifier outperforms its counterparts, proving to be the most effective in accurately classifying emails and maintaining a balance between sensitivity and specificity. The study’s implications highlight the potential for sophisticated machine learning-based spam filters to enhance email security.

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data.

This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. The primary objective was to correlate wearable device data with subjective sleep quality perceptions. Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier. Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%. More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook.

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

A Data Ecosystem for Orchard Research and Early Fruit Traceability

Advances in measurement systems and technologies are being avidly taken up in perennial tree crop research and industry applications. However, there is a lack of a standard model to support streamlined management and integration of the data generated from advanced measurement systems used in tree crop research. Furthermore, the rapid expansion in the diversity and volumes of data is increasingly highlighting the requirement for a comprehensive data model and an ecosystem for efficient orchard management and decision-making. This research focuses on the design and implementation of a novel proof-of-concept data ecosystem that enables improved data storage, management, integration, processing, analysis, and usage. Contemporary technologies proliferating in other sectors but that have had limited adoption in agricultural research have been incorporated into the model. The core of the proposed solution is a service-oriented API-driven system coupled with a standard-based digital orchard model. Applying this solution in Agriculture Victoria’s Tatura tree crop research farm (the Tatura SmartFarm) has significantly reduced overheads in research data management, enhanced analysis, and improved data resolution. This is demonstrated by the preliminary results presented for in-orchard and postharvest data collection applications. The data ecosystem developed as part of this research also establishes a foundation for early fruit traceability across industry and research.

Big data in oncology: Challenges and solutions.

6513 Background: Oncology care is complex and often multimodal. With recent technological advances, only a fraction of data is structured feasibly for research. Here we present a step-by-step method of building a novel comprehensive pan-cancer oncology data model using standard data definitions and industry-standard benchmarks. Methods: A team of 133 members was assembled including a project manager, bioinformatic engineers, business analysts, biostatisticians, data stewardship experts, clinical curators, and quality assurance (QA) managers. We first identified data domains that capture a comprehensive patient journey, leveraging existing oncology data models as a starting point, including NAACCR, PRISSMM (Deb Schrag & Eva Lepisto), and mCODE (ASCO). A common data model was developed using standard terms plus 5-10 disease specific elements (DSE). REDCap was used as the database platform, as it is HIPAA compliant and allows customizations. The data was stored in AuroraDB using an architecture and products that provide scalability from both an integration and consumption perspective. Results: We identified 10 data domains, including 186 distinct elements: demographics (20), comorbidities (2); cancer diagnosis & staging (27), pathology (45), imaging (18), medications (11), oncology responses (11), radiation treatments (14), cancer surgeries (11), cancer genomic (19), tumor markers (8), and vitals (8). Standard ontologies were used, including ICD-0-3 histology codes, ICD-10 comorbidities codes, CPT cancer surgeries codes, and CTCAE 5.0 for toxicities. We identified a data steward for each tumor type across medical oncology, surgery, pathology, radiology, and radiation oncology domains who aided curator training and the identification of DSE. QA managers and analysts performed 20% source data verification. In addition, we built REDCap rules (applicable across a form), and complex queries (applicable across multiple forms). To support QA and clinical engagement, interactive Tableau dashboards were constructed. In addition, timing and quality errors were monitored via Tableau dashboards at the individual curator level to provide timely feedback, leading to improved data quality and curation efficiency in real time. The Medical oncology and radiology domains were the most time-consuming, whereas Cancer diagnosis was the most difficult to curate. Conclusions: We collected genomic and phenomic data for 15,579 patients across six tumor types to date. Collecting comprehensive oncology data across tumor types is possible but requires institutional support, collaboration between clinical & informatics teams, and a dedicated QA team. [Table: see text]

Water Quality Modeling in Headwater Catchments: Comprehensive Data Assessment, Model Development and Simulation of Scenarios

Water quality is a major concern globally and in headwater catchments of developing countries it is often poorly managed. In these catchments, having scarce and heterogeneous information hinders the development of water quality assessments and predictive models to support management. To address this issue, the authors propose a framework of three stages that allows for: (i) conducting a comprehensive assessment of water quality; (ii) the development of a mountain stream water quality model based on said assessment; and (iii) the simulation of scenarios with the model to resolve conflicts between uses and quality of water. The framework involves multivariate analyses of principal components and clusters and follows a novel modeling protocol mainly designed for mountainous streams in developing countries. Applied to an Andean catchment in Colombia, the first stage of the framework revealed the catchment’s most significant water quality constituents and the most polluted season. The problematic constituents in this catchment include pathogens, nutrients, organic matter, and metals such as the highly toxic Cr and Pb, while water pollution is the highest during the driest months of the year (i.e., January to March). In the second stage, the model was calibrated reproducing the concentrations of pathogens, organic matter, and most nutrients, and showed a predictive capacity. This capacity was measured with an objective function to be minimized based on a normalized root mean square error. It increased only 14% when verified with a different dataset. In addition, during the third stage of the proposed framework, the simulation of alternative scenarios showed that centralized treatment is not sufficient to make water safe for potabilization and agriculture in the catchment. For this reason, improving water quality in the sub-basins at the highest altitudes is required. The proposed framework can be applied in other headwater catchments where information is limited, and where an improved management of water quality is needed.

Automated data integration of residential and commercial PV systems into DSO SCADA utilising IEC 61850 compliant comprehensive data model

As a vital part of the ongoing energy transition, an increasing number of residential as well as small and medium-sized commercial PV systems are and will be installed in the distribution grids. In terms of PV integration, the physical integration of grid-interconnected PV systems often stands in the focus. However, system integration on the information level must be handled as well regarding the scalability of decentralised energy systems. It is not feasible to manually integrate millions of decentralised PV systems into the utility grid management systems. Hence, a model-driven automation approach should be considered to standardise the integration process of distributed PV systems. This paper investigates this issue at the data model and communication level. Two highlights are: 1) the utilisation of an IEC 61850 compliant comprehensive data model for advanced use cases, such as PV curtailment and application of PV forecast; 2) an IEC 61850 model-based automation framework as the data model interface for an experimental distribution grid control centre. The proposed concept has been validated at different testing levels and could assist DSOs in handling the controllability and scalability of distributed PV systems, realise the high capacity utilisation scenarios of future distribution grids and reduce grid reinforcement investments.

Enabling Process Mining In Global Production Networks

In today's globalized economy, global production networks (GPN) play a central role in ensuring competitiveness. The number of interconnections and size of the networks lead to an ever-increasing complexity. With the help of data-driven methods, such as process mining, transparency over material flows in GPNs can be increased, making the complexity manageable. However, the adoption of process mining is associated with challenges. In GPN, different components are produced across various production stages, each with their own characteristics. In the course of the material flow, the components are then assembled into component groups, which in turn represent intermediate products or the finished end product. Consequently, converging material flows are the result. These circumstances make it difficult to build unique case identifiers and a comprehensive process data model. Furthermore, the identification of the right data sources is a challenge, since in GPN often different information systems are available in individual production lines or locations. We present an approach that supports overcoming these challenges and enables process mining in GPNs. For this purpose, the presented approach contains a process data model that takes into account all GPN specifics and requirements of process mining. This includes the handling of different component types and their properties as well as the merging of process instances in converging material flows. Through this, the individual sub-components of a finished product with their respective sub-processes are traceable. Further, we provide a procedure that supports the user in data identification and extraction by describing common data structures in information systems, which are usable for the generation of process mining event logs in GPN. Based on these data structures, we provide standardized input data tables into which the identified raw data can be extracted. Subsequently, the transfer into the presented process data model is automated by a data transformation algorithm, which takes into account the specifics of material flows in GPN. The approach was applied, implemented and validated in the GPN of an automotive manufacturer.

Digital Twin applications using the SIMULTAN data model and Python

Python is an open, general-purpose programming language that is used in many tools, libraries and APIs for Building Performance Simulations (BPS). Advantages of Python in the context of digital twins are the simple and powerful capabilities to generate input files, automate processes, import libraries in many languages and a large number of useful modules. However, in order to use BPS tools and libraries with real time data, a comprehensive data model is required in which all necessary data such as geometry, system engineering, databases, sensors, or simulation parameters for the different BPS are defined. Python in combination with SIMULTAN as a suitable open Building Information Modelling (BIM) data model allows an effective use of these tools and libraries to perform and automate analyses. This paper presents a Python module that integrates the SIMULTAN model in Python and enables almost seamless integration with minor adaptations to existing tools or modules. The import is achieved using simple text-based templates for the data types and their mapping in the data model. The data model, the definition of the data types and the use of this module is demonstrated by calculating the trend of the CO2 concentration in a zone of a digital twin using real time data.

Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model

In order to improve the development and construction level of colleges and universities and help teachers improve their scientific and technological abilities, a model based on deep learning model for the extraction and analysis of factors affecting the improvement of college teachers’ scientific and technological abilities was proposed. This article analyzes the data of teaching evaluation and finds that the text contains students’ subjective understanding of teachers’ teaching quality defects. By extracting key words from students’ teaching evaluation texts and combining with the teaching evaluation indexes, a teaching evaluation label system integrating teaching evaluation texts is designed. In order to find the defects of teachers’ teaching ability, this article, based on the principle of data portrait, combines the characteristics of teachers’ personal basic information, curriculum information, teaching evaluation information, and social relations to portrait teachers. The experimental results show that the F1 value extracted from the evaluation labels fluctuates in the evaluation text data of different colleges, with the lowest value of 91.7% in the School of Statistics and the highest value of 95.8% in the School of Foreign Languages. The algorithm in this article has a higher F1 value of the evaluation label vector extracted from the evaluation text of different grades. F1 values showed a trend of gradient decline with the increase of grade, and the decreasing range became bigger and bigger. Conclusion. The constructed teacher portraits are more accurate and effective, and provide a comprehensive and effective data model for teaching ability improvement method recommendation strategy.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model.

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

Reasonable and effective regional ecological evaluation and analysis methods can be an effective help for urban sustainable development, but there are still some errors in the current ecological prediction and analysis methods. To solve this problem, this paper proposes a prediction method of per capita ecological carrying capacity based on the autoregressive integrated moving average model (ARIMA) and long short-term memory (LSTM). First, the method improves the ecological footprint model based on energy analysis and constructs a comprehensive regional ecological data model; considering the complex characteristics of ecological data set, based on the ARIMA network model and LSTM model, a reliable and efficient big data prediction model of per capita ecological carrying capacity is established by analyzing the linear or nonlinear data sets in the data set. Finally, according to the actual ecological data set collected in Shenzhen, China, the results show that the economic and ecological trend of Shenzhen is generally good.

Dynamic Early-Warning Model of College Students’ Psychological Crisis Based on Characteristic Attribute

At present, the existing dynamic early-warning model of college students' psychological crisis cannot obtain psychological characteristics, and the error of psychological crisis prediction is large. The early-warning result of psychological crisis appears big deviation. A new dynamic early-warning model of college students' psychological crisis is designed based on characteristic attributes. In the process of modeling negative psychological crisis of college students, the fusion constraint fuzzy theory obtains the essence of college students' psychology from subjective and objective reasons, gives the different stages of college students' psychology, obtains the corresponding information entropy of the behavior hindrance in each stage of college students' psychology, and calculates the threshold value of the behavior tendency of college students' psychology. Based on the psychological state information of college students obtained by perception, the fuzzy comprehensive early-warning data model of feature attributes is constructed. According to the fuzzy comprehensive early-warning method and the early-warning index system, the early-warning factor set of college students' psychological crisis is constructed, and the dynamic early-warning model of college students' psychological crisis is constructed. Experimental results show that the mining results are consistent with the actual situation, and the grey relational degree, small error probability, and mean square ratio index are better than the present model.

Modelling and integrating public-law restrictions on urban areas for future cadastral system of Turkey

The importance of evaluating cities with historical, environmental and liveability parameters have become critical for effective urban management. Modern cadastre and land administration need to register Public-Law Restrictions (PLRs) to ensure the completeness of various transactions. To develop a comprehensive PLR data model for real estate management integrated with National GDI models, this study investigates the urban-related PLRs. An interoperable data model was designed within the Management-Restriction-Regulation Zones data model as an extension with relation to the Cadastre data model. In future cadastral applications, all property-related restrictions can be accessible as in case of the designed data model.

Visual Exploration of Regional Factors of the Health of Urban Residents

Urban healthcare operations continuously generate large amounts of electronic medical record data. Combined with urban regional factors, a comprehensive data model is established to conduct a multidimensional visual analysis, which is helpful in extracting and analyzing the correlations between regional factors and residents’ health. This paper establishes data models for regional factors such as regional diet, industrial economy, climate, and regional epidemics and introduces an Apriori association algorithm to find disease association relationships, a one-mode projection algorithm for bipartite networks to perform disease clustering, and a collaborative filtering recommendation algorithm to predict patients’ potential high-risk diseases. Combined with the general analysis process for the visual exploration of regional health impact factors proposed in this paper, a visual analysis system is designed, and through the collaborative interaction of multiple visual interfaces, the multidimensional visual presentation of the association pattern between regional factors and residents’ health is achieved. Taking a sample size of 24,626 electronic medical records in a city as an example, the case study proves that the exploration of regional factors such as catering, climate and regional epidemics can provide an analytical basis and decision support for improving the health level of urban residents.

3D TOPOLOGICAL SUPPORT IN SPATIAL DATABASES: AN OVERVIEW

Abstract. The storage of spatial data that consists of spatial and non-spatial properties requires a database management system that possesses spatial functions that can cater to the spatial characteristics of data. These characteristics include the geometrical shape, topological and positional information. Parallel to how geometries describe the shape of an object, topological information is also an important spatial property which describes how the geometries in a space are related to each other. This information describes the connectivity, containment and adjacencies of spatial objects which are the foundation for more complex analysis such as navigation, data reconstruction, spatial queries and others. However, the topological support provided by spatial databases varies. This paper provided an overview on the current implementations of topological support in spatial databases such as ArcGIS, QGIS, PostgreSQL and others. The native topology in most spatial databases was found to be 2D topology maintained by 2D topology rules with limited representation of 3D topological relationships. Consequently, 3D objects represented by 2D topology had to be decomposed into objects of lower dimensions. Approaches to implement additional topological support for spatial databases included the use of topological data models, data structures, operators, and rules. 3D applications such as 3D cadastre required more detailed representations of topological information which required a more comprehensive 3D topological data model. Nonetheless, comprehensive preservation of topological information also mandates voluminous storage and higher computational efficiency. Thus, the appropriate 3D topological support should be provided in spatial databases to accurately represent 3D objects and meet 3D analysis requirements.