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The Main Concept of the Entity-Attribute-Value Model. The entity-attribute-value serves for storing the structure of the information along with the actual values. This model mainly consists of three relations: The relations Entity and Attribute contain information about entities and their attributes and the relation Value stores the actual values for occurring entity-attribute pairs.

The Main Concept of the Entity-Attribute-Value Model. The entity-attribute-value serves for storing the structure of the information along with the actual values. This model mainly consists of three relations: The relations Entity and Attribute contain information about entities and their attributes and the relation Value stores the actual values for occurring entity-attribute pairs.

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Background For an optimal care of patients in home healthcare, it is essential to exchange healthcare-related information with other stakeholders. Unfortunately, paper-based documentation procedures as well as the heterogeneity between information systems inhibit a well-regulated communication. Therefore, a digital patient care record is introduced...

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... entity-attribute-value model comprises three basic relations (as shown in Figure 2): the actual data are stored in the relation value whereas the entities and the attributes are stored in the relations entity and attribute, respectively. ...

Citations

... The purpose of this project was to investigate feasibility of a data science approach, using routinely collected and deidentified autopsy data, to determine which elements are most contributory to determining a medical cause of death, in order to both develop future operational strategies to increase procedural efficiency and to provide objective information which could potentially be used both for planning and counselling parents and families. This included specifically; to extract data from the existing research database (MS Access) into an entity attribute value schema to optimise data analytics [6] and the efficiency of storing data [7] as well as flexibility for health care data, to apply a Decision Tree analytical method to the extracted data (Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable). [8] We further explore ensemble methods which combine techniques to balance variance versus bias, [9] including Random Forests, in which All rights reserved. ...
Article
Introduction Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods A paediatric autopsy database containing >7,000 cases, with >300 variables, was analysed by examination stage and autopsy outcome classified as ‘explained (medical cause of death identified)’ or ‘unexplained’. Decision tree, random forest, and gradient boosting models were iteratively trained and evaluated. Results Data from 3,100 infant and young child (<2 years) autopsies were included. Naïve decision tree using external examination data had performance of 68% for predicting an explained death. Core data items were identified using model feature importance. The most effective model was XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular and respiratory histological findings as the most important variables associated with determining medical cause of death. Conclusion This study demonstrates feasibility of using machine-learning to evaluate component importance of complex medical procedures (paediatric autopsy) and highlights value of collecting routine clinical data according to defined standards. This approach can be applied to a range of clinical and operational healthcare scenarios
... One possibility to deal with this would be to adopt the EAV model, where separate tables are used to store entity-attribute-value triples, thus encouraging flexibility and extensibility. The EAV model is often used in the healthcare domain to store and manage highly-sparse patient data in a compact way [27]. However, here we chose not to adopt the EAV model for three reasons: (i) it would lead to a significant deviation from a classical star schema; (ii) it would add significant burden to the formulation of OLAP queries; (iii) it is known to cause performance issues in presence of large volumes of data -which indeed is normally the case in DWs. ...
Article
Multi-model DBMSs (MMDBMSs) have been recently introduced to store and seamlessly query heterogeneous data (structured, semi-structured, graph-based, etc.) in their native form, aimed at effectively preserving their variety. Unfortunately, when it comes to analyzing these data, traditional data warehouses (DWs) and OLAP systems fall short because they rely on relational DBMSs for storage and querying, thus constraining data variety into the rigidity of a structured, fixed schema. In this paper, we investigate the performances of an MMDBMS when used to store multidimensional data for OLAP analyses. A multi-model DW would store each of its elements according to its native model; among the benefits we envision for this solution, that of bridging the architectural gap between data lakes and DWs, that of reducing the cost for ETL, and that of ensuring better flexibility, extensibility, and evolvability thanks to the combined use of structured and schemaless data. To support our investigation we define a multidimensional schema for the UniBench benchmark dataset and an ad-hoc OLAP workload for it. Then we propose and compare three logical solutions implemented on the PostgreSQL multi-model DBMS: one that extends a star schema with JSON, XML, graph-based, and key–value data; one based on a classical (fully relational) star schema; and one where all data are kept in their native form (no relational data are introduced). As expected, the full-relational implementation generally performs better than the multi-model one, but this is balanced by the benefits of MMDBMSs in dealing with variety. Finally, we give our perspective view of the research on this topic.
... Unlike information models dedicated to the biomedical field that offer specific predefined objects for hospital information systems or electronic health records (OpenEHR, FHIR…), the EAV model is a generic model, considered flexible enough to model biomedical data [36][37][38]. From a logical point of view, data models, whether relational or object-oriented, can be translated into the EAV model. ...
... The design of the BCKM was done in the context of the development of a research prototype with the strong constraint of representing real data from the DESIMS (that relies on a relational model). We chose to use the EAV data model, which is simple but generic and flexible enough to model biomedical data [36][37][38]. In addition, we chose to explain each element of the EAV model as classes and not exploit the OWL possibilities where attributes could have been represented by DataProperties for primitive types and ObjectProperties for hierarchical types. ...
Article
The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guideline-based decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, “refinement” and “complement”, used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75% of the cases.
... The purpose of this project was to investigate feasibility of a data science approach, using routinely collected and deidentified autopsy data, to determine which elements are most contributory to determining a medical cause of death, in order to both develop future operational strategies to increase procedural efficiency and to provide objective information which could potentially be used both for planning and counselling parents and families. This included specifically; to extract data from the existing research database (MS Access) into an entity attribute value schema to optimise data analytics [6] and the efficiency of storing data [7] as well as flexibility for health care data, to apply a Decision Tree analytical method to the extracted data (Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable). [8] We further explore ensemble methods which combine techniques to balance variance versus bias, [9] including Random Forests, in which All rights reserved. ...
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Introduction: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death, yet despite full postmortem examination (autopsy), the cause of death is only determined in around 45% of cases, the majority remaining unexplained. In order to aid counselling and understand how to improve the investigation, we explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods: A paediatric autopsy database containing >7,000 cases in total with >300 variables per case, was analysed with cases categorised both by stage of examination (external, internal and internal with histology), and autopsy outcome classified as explained-(medical cause of death identified) or unexplained. For the purposes of this study only cases from infant and child deaths aged ≤ 2 years were included (N=3100). Following this, decision tree, random forest, and gradient boosting models were iteratively trained and evaluated for each stage of the post-mortem examination and compared using predictive accuracy metrics. Results: Data from 3,100 infant and young child autopsies were included. The naive decision tree model using initial external examination data had a predictive performance of 68% for determining whether a medical cause of death could be identified. Model performance increased when internal examination data was included and a core set of data items were identified using model feature importance as key variables for determining autopsy outcome. The most effective model was the XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular or respiratory histological findings as the most important variables associated with determining cause of death. Conclusion: This study demonstrates the feasibility of using machine learning models to objectively determine component importance of complex medical procedures, in this case infant autopsy, to inform clinical practice. It further highlights the value of collecting routine clinical procedural data according to defined standards. This approach can be applied to a wide range of clinical and operational healthcare scenarios providing objective, evidence-based information for uses such counselling, decision making and policy development.
... D'un point de vue logique, les modèles de données, qu'ils soient relationnels ou objet, peuvent être traduits dans le modèle EAV. Il est considéré comme suffisamment flexible pour modéliser des données biomédicales (Nadkarni et al., 1999 ;Löper et al., 2013 ;Khan et al., 2014). Nous avons donc structuré l'ontologie au travers du prisme du modèle EAV et nous avons choisi de représenter explicitement sous forme de classes/concepts les trois éléments du modèle EAV, les entités, les attributs et les valeurs. ...
Article
Dans un objectif d’amélioration de la qualité des soins, nous avons développé une plateforme proposant trois systèmes d’aide à la décision clinique (SADC) basés sur des approches complémentaires, (i) un SADC fondé sur les guides de bonne pratique (GBP) dont l’objectif est de rappeler les recommandations issues de l’état de l’art dans une approche centrée patient, (ii) un SADC fondé sur l’expérience acquise lorsque les utilisateurs cliniciens informés décident de ne pas suivre les GBP et prennent des décisions non conformes pour lesquelles ils doivent fournir une justification, et (iii) un SADC fondé sur la mise en œuvre d’un raisonnement à partir de cas. Les interactions entre les trois SADC sont contrôlées par une ontologie du domaine. Utilisée comme une structure conceptuelle et terminologique, elle propose un modèle de données générique (entité-attribut-valeur) et un modèle de connaissances utilisés pour le raisonnement ontologique (subsomption) et l’aide à la décision déductive (inférence). La plateforme a été développée pour améliorer la prise en charge du cancer du sein dans le cadre du projet européen DESIREE.
... The EAV model is increasingly used for knowledge representation for complex heterogeneous biomedical databases [8]. It allows for highly sparse data to be stored in a compact way [9]. ...
Conference Paper
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A user interface tailored to the specific needs of individual installations and swift fulfilment of user requirements contribute to the level of user satisfaction. Both can be achieved using dynamic user interfaces. The proposed framework consists of a module for data structure definition and a designing module in which graphical components can be bound to the data and assembled into user interfaces using drag-and-drop, while a built-in rule engine enables additional operations. It allows the end user to define the data model and design the corresponding input forms and reports. It is especially beneficial in cases where there is a need for frequent changes in the data structure and the graphical layout. Such an approach saves time and enables the development and modification of application modules without the involvement of programmers. The framework has been applied successfully for report generation, creating the medical documentation and modeling business processes in several domains.
... With a few exceptions [29,44,45], the database performance (i.e. query speed, accuracy and throughput) and data usability (i.e. ...
... In biomedical data capture and archiving practice, both RDBMS and NoSQL DBMS have been used to build database tools [28,34,38,[60][61][62]. In the RDBMS environment, a particular data structure, EAV, has been a popular strategy to 'boost' database flexibility [24,28,29,45,61]. However, researchers and users soon noticed system performance and data usability problems [29,[63][64][65]. ...
Article
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Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.
Conference Paper
Medical data is stored across institutions in heterogeneous systems with differences in both, data structures and their semantics. Often, additional information from other data sources is required, e.g. for decision making. The extension of all the data combined from different sources represents the global data or global knowledge. Thus, granting participants access to this global knowledge is crucial for a successful clinical treatment. Accessing this information through the local system is called global-as-local-view-extension. This paper presents an approach for realizing this by using the entity-attribute-value model in accordance with a special schema mapping technique as well as inverses of schema mappings between local and global repositories.