Performance Analysis and Assessment of a TF-IDF Based Archetype-SNOMED-CT Binding Algorithm

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors\u27 automatic search and used for assessing the algorithm\u27s performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process

Clinical coverage of an archetype repository over SNOMED-CT

AbstractClinical archetypes provide a means for health professionals to design what should be communicated as part of an Electronic Health Record (EHR). An ever-growing number of archetype definitions follow this health information modelling approach, and this international archetype resource will eventually cover a large number of clinical concepts. On the other hand, clinical terminology systems that can be referenced by archetypes also have a wide coverage over many types of health-care information.No existing work measures the clinical content coverage of archetypes using terminology systems as a metric. Archetype authors require guidance to identify under-covered clinical areas that may need to be the focus of further modelling effort according to this paradigm.This paper develops a first map of SNOMED-CT concepts covered by archetypes in a repository by creating a so-called terminological Shadow. This is achieved by mapping appropriate SNOMED-CT concepts from all nodes that contain archetype terms, finding the top two category levels of the mapped concepts in the SNOMED-CT hierarchy, and calculating the coverage of each category. A quantitative study of the results compares the coverage of different categories to identify relatively under-covered as well as well-covered areas. The results show that the coverage of the well-known National Health Service (NHS) Connecting for Health (CfH) archetype repository on all categories of SNOMED-CT is not equally balanced. Categories worth investigating emerged at different points on the coverage spectrum, including well-covered categories such as Attributes, Qualifier value, under-covered categories such as Microorganism, Kingdom animalia, and categories that are not covered at all such as Cardiovascular drug (product)

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Automatic Event Geo-Location in Twitter

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Métodos semánticos automatizados de apoyo a la gestión y a la interoperabilidad de la información clínica

Uno de los retos actuales de la informática médica es lograr la interoperabilidad semántica entre los sistemas de información de distintas instituciones sanitarias. La interoperabilidad completa permitirá que los sistemas intercambien y comprendan automáticamente la información de los pacientes y facilitará el acceso completo a la información de un paciente desde cualquier sistema o institución. Varios proyectos a nivel europeo han definido una hoja de ruta con recomendaciones y acciones encaminadas a lograr la interoperabilidad semántica de la Historia Clínica Electrónica (HCE), entre las que se incluyen: (i) el uso de modelos de datos clínicos estructurados (arquetipos) para capturar de forma ordenada y sistemática la información de pacientes en escenarios clínicos determinados y (ii) la integración de terminologías clínicas dentro de la HCE, principalmente mediante la creación de enlaces entre ítems de información clínica (presentes en los modelos de datos de la HCE) con conceptos estándar de las terminologías. La tesis se centra en el desarrollo de métodos avanzados para enlazar de forma automática la información clínica de la HCE, especialmente de arquetipos, con la terminología SNOMED-CT. También, se han desarrollado aplicaciones que demuestran las ventajas de integrar una terminología de referencia en repositorios de datos clínicos. La tesis demuestra que es factible automatizar el enlazado entre la información clínica de los arquetipos y SNOMED-CT, con una precisión y cobertura elevada, si se hace uso de la información contextual y estructural implícita en los arquetipos y en SNOMED-CT

pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy

Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience. This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes. Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today

Performance analysis and assessment of a tf-idf based archetype-SNOMED-CT binding algorithm

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors\u27 automatic search and used for assessing the algorithm\u27s performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process