empathi: An ontology for Emergency Managing and Planning about Hazard Crisis

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/

Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

The current work addresses the unifi cation of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifi es multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing fl exibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profi le) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the defi nition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains

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Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health