220 research outputs found

    Improving the Quality and Utility of Electronic Health Record Data through Ontologies

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    The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstÞtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er Ä designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede lÞsningen er fokusert pÄ forbedring av fysisk aktivitet. Prototypen bruker bÊrbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for Ä utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen pÄ teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Knowledge graph embedding enhancement using ontological knowledge in the biomedical domain

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    The biomedical field is a critical area for natural language processing (NLP) applications because it involves a vast amount of unstructured data, including clinical notes, medical publications, and electronic health records. NLP techniques can help extract valuable information from these documents, such as disease symptoms, medication usage, and treatment outcomes, which can improve patient care and clinical decision-making. MAPS S.p.A. currently produces Clinika, a software that extracts knowledge from clinical corpora. Clinika performs the task of Named Entity Recognition (NER) by linking entities to medical concepts from an established knowledge base, in this case, the Unified Medical Language System (UMLS). This dissertation details how we approached designing and implementing a component for the new version of Clinika, specifically a mention embedder that uses embeddings to perform entity linking with UMLS concepts. We focused on enhancing existing dense contextual embeddings by injecting ontological knowledge, using two parallel approaches: (1) taking the embeddings as a by-product of an entity alignment model aided by an ontology, and (2) fine-tuning a contextual language model with custom sampling strategies. We evaluated both approaches with suitable experiments from the relevant literature. After testing, we discontinued the first approach but found more significant results using the second. The results on the tasks chosen to evaluate the embeddings were not promising, we address the causes in the Error Analysis section, and discuss further work on this topic

    Translation and/in development: promoting more effective policy Interventions in Vietnam

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    Development necessitates specialised communication involving multiple actors at many levels, especially in Global South contexts. However, this communication is hindered because the discourse, concepts and terminology of development developed in the West are introduced into communities in many parts of the world, including Vietnam, with little evidence that they are being understood or used as intended. Problematic translations of development concepts could have significant real-word impacts on the multidirectional communication between key stakeholders involved in development, impede policy-making and prevent the implementation of development initiatives at local levels. This interdisciplinary PhD project—combining perspectives from Translation Studies and Development Studies with insights gained from real-world development practice—addresses the problem space of communication and mutual understanding in development settings to answer the following overall research question: What role(s) do translation and terminology have in development practice and policy in Vietnam? This research was undertaken using a methodology that combined a case study approach with an ethnographic orientation. Data from in-depth, online interviews with 18 development stakeholders in Vietnam were triangulated with analysis of a 1.1 million-word corpus of development texts, the researcher’s autoethnographic accounts, grey literature, and a specially-designed workshop for stakeholders. Findings suggest that translation of key development concepts in Vietnam is problematic with under-recognised impacts on development practice and policy, and this situation could be improved through policy interventions, better tool use, new translation workflows and practices, and greater shared learning. Overall, analysis in this study suggests that translation and terminology are used by various stakeholders in Vietnam as important enablers to local participation and ownership, achieve meaningful development outcomes through local empowerment and contribute to the decolonisation of development. Keywords: Translation, terminology, development practice, development policy, Vietnam, interdisciplinarity, vernacular knowledge, practice theory

    Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

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    In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

    Automatic Terminology Coding for the Biomedical Domain

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    The biomedical sector, rich in unstructured data from sources like clinical notes and health records, presents a prime opportunity for Natural Language Processing (NLP) applications. Especially pivotal is the task of entity linking, wherein textual mentions are mapped to medical concepts within a knowledge base, in this case, represented by the Unified Medical Language System (UMLS) Metathesaurus. Within this realm, the Italian language faces resource constraints (only 4% of UMLS 4M concepts have a label in the Italian language). Current systems like MAPS Group’s Clinika software lean on label matching to link the extracted facts to the corresponding UMLS concepts. This dissertation deals with the design of a new Clinika component aimed at enhancing entity linking for Italian terms against UMLS, even in the absence of direct Italian labels. Employing transformer-based multilingual embeddings, a novel 'concept guesser' architecture was developed to tackle the linking challenge intelligently, maximizing the level of exploitation of the currently available knowledge. This innovation not only enhances Clinika’s effectiveness but also paves the way for advanced multilingual clinical decision support systems

    Impact of language skills and system experience on medical information retrieval

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    Vers l’élaboration d’un systĂšme d’organisation des connaissances en allergologie : l’analyse des documents et des pratiques informationnelles des acteurs

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    The aim of the present thesis, funded by the Occitanie Region (2019-2023), is to construct a knowledge organization system (KOS) for the Allergy Unit of the Montpellier University Hospital (France), to represent and organize the complexity of allergy knowledge. Currently, there is no KOS that might be used by allergy professionals and researchers for their information processing and seeking activities. Allergy knowledge, produced by different actors, is abundant and heterogeneous, and keeps developing in parallel with the massification of health data. To allow and provide access to this knowledge, it is crucial to identify and characterize it, first by focusing on what might be useful for professionals’ daily activities and then by structuring it in a system of organization and documentary representation possibly linking the different ways of representing knowledge by different actors in this domain. Therefore, we propose a KOS in allergy, reached through a contextualized approach that relies, on one hand, on the analysis of the context of use of specialized knowledge, by the study of the informational practices of professionals who seek, produce, and mobilize knowledge in the domain; and on the other hand, on the analysis of a corpus of documents that professionals use in their daily activities. Through our work, we perform an epistemological reflection within the Information & Communication Sciences, by showing how the analysis of informational practices contributes to the construction of a KOS for a medical domain. Moreover, we try to answer a methodological question, linked with the development of a KOS in allergy, and evaluate if our conception method, oriented by a contextualized approach, allows to propose a useful KOS for the practices of the actors in this domain

    A Step Toward Improving Healthcare Information Integration & Decision Support: Ontology, Sustainability and Resilience

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    The healthcare industry is a complex system with numerous stakeholders, including patients, providers, insurers, and government agencies. To improve healthcare quality and population well-being, there is a growing need to leverage data and IT (Information Technology) to support better decision-making. Healthcare information systems (HIS) are developed to store, process, and disseminate healthcare data. One of the main challenges with HIS is effectively managing the large amounts of data to support decision-making. This requires integrating data from disparate sources, such as electronic health records, clinical trials, and research databases. Ontology is one approach to address this challenge. However, understanding ontology in the healthcare domain is complex and difficult. Another challenge is to use HIS on scheduling and resource allocation in a sustainable and resilient way that meets multiple conflicting objectives. This is especially important in times of crisis when demand for resources may be high, and supply may be limited. This research thesis aims to explore ontology theory and develop a methodology for constructing HIS that can effectively support better decision-making in terms of scheduling and resource allocation while considering system resiliency and social sustainability. The objectives of the thesis are: (1) studying the theory of ontology in healthcare data and developing a deep model for constructing HIS; (2) advancing our understanding of healthcare system resiliency and social sustainability; (3) developing a methodology for scheduling with multi-objectives; and (4) developing a methodology for resource allocation with multi-objectives. The following conclusions can be drawn from the research results: (1) A data model for rich semantics and easy data integration can be created with a clearer definition of the scope and applicability of ontology; (2) A healthcare system's resilience and sustainability can be significantly increased by the suggested design principles; (3) Through careful consideration of both efficiency and patients' experiences and a novel optimization algorithm, a scheduling problem can be made more patient-accessible; (4) A systematic approach to evaluating efficiency, sustainability, and resilience enables the simultaneous optimization of all three criteria at the system design stage, leading to more efficient distributions of resources and locations for healthcare facilities. The contributions of the thesis can be summarized as follows. Scientifically, this thesis work has expanded our knowledge of ontology and data modelling, as well as our comprehension of the healthcare system's resilience and sustainability. Technologically or methodologically, the work has advanced the state of knowledge for system modelling and decision-making. Overall, this thesis examines the characteristics of healthcare systems from a system viewpoint. Three ideas in this thesis—the ontology-based data modelling approach, multi-objective optimization models, and the algorithms for solving the models—can be adapted and used to affect different aspects of disparate systems
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