Medical Big Data Analysis in Hospital Information System

The rapidly increasing medical data generated from hospital information system (HIS) signifies the era of Big Data in the healthcare domain. These data hold great value to the workflow management, patient care and treatment, scientific research, and education in the healthcare industry. However, the complex, distributed, and highly interdisciplinary nature of medical data has underscored the limitations of traditional data analysis capabilities of data accessing, storage, processing, analyzing, distributing, and sharing. New and efficient technologies are becoming necessary to obtain the wealth of information and knowledge underlying medical Big Data. This chapter discusses medical Big Data analysis in HIS, including an introduction to the fundamental concepts, related platforms and technologies of medical Big Data processing, and advanced Big Data processing technologies

Decision support systems for adoption in dental clinics: a survey

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

A Framework for Accessing Patient Big Data: ANT View of a South African Health Facility

This study was undertaken to develop a framework that can be used to guide analysis when translating and transforming big data into a more purposeful resource for healthcare service delivery in South Africa. It is intended to assist in addressing some of the challenges encountered by healthcare facilities. Qualitative data were collected, with a focus on the types of interactions that happen between actors when accessing and using patients’ big data for healthcare services. Actor-network theory (ANT) was applied as a lens in the analysis of the data, to examine the relationships between actors when accessing and using big data for healthcare services. From the analysis, factors influencing the accessing and use of patient’s big data were found. These factors provided the basis from which a framework was developed. The framework can be used to guide the assessment of big data and improve healthcare and service delivery to the community

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

Research protocol: EB-GIS4HEALTH UK – foundation evidence base and ontology-based framework of modular, reusable models for UK/NHS health and healthcare GIS applications

EB-GIS4HEALTH UK aims at building a UK-oriented foundation evidence base and modular conceptual models for GIS applications and programmes in health and healthcare to improve the currently poor GIS state of affairs within the NHS; help the NHS understand and harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, and requirements; and also foster the much-needed NHS-academia GIS collaboration. The project will focus on diabetes and dental care, which together account for about 11% of the annual NHS budget, and are thus important topics where GIS can help optimising resource utilisation and outcomes. Virtual e-focus groups will ensure all UK/NHS health GIS stakeholders are represented. The models will be built using Protégé ontology editor based on the best evidence pooled in the project's evidence base (from critical literature reviews and e-focus groups). We will disseminate our evidence base, GIS models, and documentation through the project's Web server. The models will be human-readable in different ways to inform NHS GIS implementers, and it will be possible to also use them to generate the necessary template databases (and even to develop "intelligent" health GIS solutions using software agents) for running the modelled applications. Our products and experience in this project will be transferable to address other national health topics based on the same principles. Our ultimate goal is to provide the NHS with practical, vendor-neutral, modular workflow models, and ready-to-use, evidence-based frameworks for developing successful GIS business plans and implementing GIS to address various health issues. NHS organisations adopting such frameworks will achieve a common understanding of spatial data and processes, which will enable them to efficiently and effectively share, compare, and integrate their data silos and results for more informed planning and better outcomes

Systematic Analysis of the Factors Contributing to the Variation and Change of the Microbiome

abstract: Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.Dissertation/ThesisDoctoral Dissertation Biology 201