搜索结果：Cardiology research and practice

Design Research Methodology (DRM) supports systematic design research through representations such as Reference Models and Impact Models. However, the practical construction and maintenance of these models often remains manual, requiring repeated redrawing, layout adjustment, and separate handling of assumptions, references, and supporting evidence. This can make DRM modelling time-consuming, visually cluttered, and difficult to revise as models increase in complexity. This paper presents DREAMS, an early-stage prototype modelling environment developed to support the creation and maintenance of DRM Reference Models and Impact Models. The tool enables users to construct typed causal models using DRM-relevant elements, define signed causal relationships, and attach assumptions, experiential inputs, and references directly to causal links. It also provides layout support and search functions to improve readability, modifiability, and retrieval of supporting information. A preliminary comparative evaluation with four DRM users was conducted against manual modelling practice. The results indicate reductions in model creation time, revision time, repositioning effort, edge crossings, and

Demographic data collection is essential in education research, as demographic data allows researchers to better describe the participant population they study and to contextualize findings. However, current research practices for neurodiversity demographics often rely on prescriptive methods (e.g., requiring participants to report official diagnoses) rather than allowing participants to self-identify. This approach can: a) not allow participants to express their intersecting identities in ways that are authentic; and b) limit trustworthiness and reliability of the data and interpretation. In addition, inconsistent dissemination and representation of demographic data across studies hinder the accessibility and usability of this work. Through a literature review of neurodivergent student experiences with learning and performing STEM, we identified widespread discrepancies in how demographic information is collected and reported. This paper explores how neurodivergent identities can be more accurately and inclusively represented in education research. We present findings of a thematic analysis on the ways neurodivergent demographic data collection is done in the literature using data

This paper presents a scientometric analysis of research output from the University of Lagos, focusing on the two decades spanning 2004 to 2023. Using bibliometric data retrieved from the Web of Science, we examine trends in publication volume, collaboration patterns, citation impact, and the most prolific authors, departments, and research domains at the university. The study reveals a consistent increase in research productivity, with the highest publication output recorded in 2023. Health Sciences, Engineering, and Social Sciences are identified as dominant fields, reflecting the university's interdisciplinary research strengths. Collaborative efforts, both locally and internationally, show a positive correlation with higher citation impact, with the United States and the United Kingdom being the leading international collaborators. Notably, open-access publications account for a significant portion of the university's research output, enhancing visibility and citation rates. The findings offer valuable insights into the university's research performance over the past two decades, providing a foundation for strategic planning and policy formulation to foster research excellence

Modern research heavily relies on software. A significant challenge researchers face is understanding the complex software used in specific research fields. We target two scenarios in this context, namely long onboarding times for newcomers and conference reviewers evaluating replication packages. We hypothesize that both scenarios can be significantly improved when there is a clear link between the paper's ideas and the code that implements them. As a time- and staff-saving approach, we propose an LLM-based automation tool that takes in a paper and the software implementing the paper, and generates a trace mapping between research ideas and their locations in code. Initial experiments have shown that the tool can generate quite useful mappings.

Over the last years, civic technology projects have emerged around the world to advance open government and community action. Although Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) communities have shown a growing interest in researching issues around civic technologies, yet most research still focuses on projects from the Global North. The goal of this workshop is, therefore, to advance CSCW research by raising awareness for the ongoing challenges and open questions around civic technology by bridging the gap between researchers and practitioners from different regions. The workshop will be organized around three central topics: (1) discuss how the local context and infrastructure affect the design, implementation, adoption, and maintenance of civic technology; (2) identify key elements of the configuration of trust among government, citizenry, and local organizations and how these elements change depending on the sociopolitical context where community engagement takes place; (3) discover what methods and strategies are best suited for conducting research on civic technologies in different contexts. These core topics will be covered across session

Hackathons are time-bounded collaborative events which have become a global phenomenon adopted by both researchers and practitioners in a plethora of contexts. Hackathon events are generally used to accelerate the development of, for example, scientific results and collaborations, communities, and innovative prototypes addressing urgent challenges. As hackathons have been adopted into many different contexts, the events have also been adapted in numerous ways corresponding to the unique needs and situations of organizers, participants and other stakeholders. While these interdisciplinary adaptions, in general affords many advantages - such as tailoring the format to specific needs - they also entail certain challenges, specifically: 1) limited exchange of best practices, 2) limited exchange of research findings, and 3) larger overarching questions that require interdisciplinary collaboration are not discovered and remain unaddressed. We call for interdisciplinary collaborations to address these challenges. As a first initiative towards this, we performed an interdisciplinary collaborative analysis in the context of a workshop at the Lorentz Center, Leiden in December 2021. In this

Background and Objectives: Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners to limit practice variations, reduce clinical cost, improve the quality of care, and provide evidence based treatment. However, extracting relevant knowledge from the plethora of CPGs is not feasible for already burdened healthcare professionals, leading to large gaps between clinical findings and real practices. It is therefore imperative that state-of-the-art Computing research, especially machine learning is used to provide artificial intelligence based solution for extracting the knowledge from CPGs and reducing the gap between healthcare research/guidelines and practice. Methods: This research presents a novel methodology for knowledge extraction from CPGs to reduce the gap and turn the latest research findings into clinical practice. First, our system classifies the CPG sentences into four classes such as condition-action, condition-consequences, action, and not-applicable based on the information presented in a sentence. We use deep learning with state-of-the-art word embedding, im

The concurrent effect of various internal and external factors on IT Outsourcing (ITO) decision making has seldom been investigated in a single study. Furthermore, research on external factors is scarce and there is no comprehensive theory that can fully explain ITO decisions made in practice. This paper explains how key decision factors, both internal and external, influence ITO decision making in a large Australian University. We also tested the feasibility of a highly regarded descriptive model of ITO decisions as the basic foundation of an ITO decision theory. The model failed to fully explain ITO decisions in our case organisation. We draw researchers' attention to the need for more exploration of external factors as well as clarification of contingency factors that may explain inconsistencies between ITO decision theories and practice, and call for more research for 'practicable' ITO decision aids. Implications for practice are also discussed in the paper.

Case-oriented physics education research - which seeks to refine and develop theory by linking that theory to cases - incorporates distinct practices for selecting data for analysis, generalizing results, and making causal claims. Unanswered questions about these practices may constrain researchers more familiar with the recurrence-oriented research paradigm - which seeks to inform instructional predictions by discerning reproducible, representative patterns and relationships - from participating in or critically engaging with case-oriented research. We use results from interviews with physics education researchers, a synthesis of the literature on research methodologies, and published examples of case-oriented and recurrence-oriented research to answer "hard-hitting questions" that researchers may pose. In doing so, we aim to substantiate our position that both case-oriented and recurrence- oriented PER are rigorous but that the rigor is of a different nature in each paradigm.

The potential disconnect between research and practice in software engineering (SE) means that the uptake of research outcomes has at times been limited. In this paper we seek to identify research approaches that are rigorous in terms of method but that are also relevant to software engineering practitioners. After considering the correspondence of several approaches to software systems research and practice we recommend a framework for applying grounded theory in SE research, as a means of delivering both robust and useful outcomes.

This report by the CRA Working Group on Socially Responsible Computing outlines guidelines for ethical and responsible research practices in computing conferences. Key areas include avoiding harm, responsible vulnerability disclosure, ethics board review, obtaining consent, accurate reporting, managing financial conflicts of interest, and the use of generative AI. The report emphasizes the need for conference organizers to adopt clear policies to ensure responsible computing research and publication, highlighting the evolving nature of these guidelines as understanding and practices in the field advance.

Heart diseases remain a leading cause of morbidity and mortality worldwide, necessitating accurate and trustworthy differential diagnosis. However, existing artificial intelligence-based diagnostic methods are often limited by insufficient cardiology knowledge, inadequate support for complex reasoning, and poor interpretability. Here we present HeartAgent, a cardiology-specific agent system designed to support a reliable and explainable differential diagnosis. HeartAgent integrates customized tools and curated data resources and orchestrates multiple specialized sub-agents to perform complex reasoning while generating transparent reasoning trajectories and verifiable supporting references. Evaluated on the MIMIC dataset and a private electronic health records cohort, HeartAgent achieved over 36% and 20% improvements over established comparative methods, in top-3 diagnostic accuracy, respectively. Additionally, clinicians assisted by HeartAgent demonstrated gains of 26.9% in diagnostic accuracy and 22.7% in explanatory quality compared with unaided experts. These results demonstrate that HeartAgent provides reliable, explainable, and clinically actionable decision support for cardio

The rapid move to remote work due to COVID-19 social distancing policies has slowed or stopped most in-person qualitative user research activities. The limitation on in-person activities has pushed many user researchers to consider how they can continue their research remotely and presents an opportunity for user research teams to (re)design their research processes for remote work. In this position statement, I outline prior work in remote qualitative user research methods and discuss how this work can help us to (re)design new tools, methods, and frameworks that can enable remote work. I then propose a set of research questions that can further our knowledge and abilities to conduct effective remote user research. With creative development, we can create remote research methods that afford new ways of understanding user experience today and build more environmentally conscious and accessible research practices for a future where remote user research is much more common.

Based on an ethnographic action research study for a Digital Twin (DT) deployment on an automated highway maintenance project, this paper reports some of the stumbling blocks that practitioners face while deploying a DT in practice. At the outset, the scope of the case study was broadly defined in terms of digitalization, and software development and deployment, which later pivoted towards the concept of Digital Twin during the collective reflection sessions between the project participants. Through an iterative learning cycle via discussions among the various project stakeholders, the case study led to uncovering the roadblocks in practice faced by the Architecture, Engineering, and Construction (AEC) practitioners. This research finds that the practitioners are facing difficulty in: (1) Creating a shared understanding due to the lack of consensus on the Digital Twin concept, (2) Adapting and investing in Digital Twin due to inability to exhaustively evaluate and select the appropriate capabilities in a Digital Twin, and (3) Allocation of resources for Digital Twin development due to the inability to assess the impact of DT on the organizational conditions and processes.

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.

The production of knowledge has become increasingly a global endeavor. Yet, location related factors, such as local working environment and national policy designs, may continue to affect what kind of science is being pursued. Here we examine the geography of the production of creative science by country, through the lens of novelty and atypicality proposed in Uzzi et al. (2013). We quantify a country's representativeness in novel and atypical science, finding persistent differences in propensity to generate creative works, even among developed countries that are large producers in science. We further cluster countries based on how their tendency to publish novel science changes over time, identifying one group of emerging countries. Our analyses point out the recent emergence of China not only as a large producer in science but also as a leader that disproportionately produces more novel and atypical research. Discipline specific analysis indicates that China's over-production of atypical science is limited to a few disciplines, especially its most prolific ones like materials science and chemistry.

The present study attempts to highlight the research output generated in Russia in coronary artery disease (CAD) research during the period 1990-2019 to understand the distribution of research output, top journals for publications, and most prolific authors, authorship pattern, and citation pattern. This study is based on secondary data extracted from the Science Citation Index (SCI), which is an integral component of the Web of Science. Descriptive and inferential statistical techniques were applied in the study. There were 5058 articles by Russian scholars in coronary artery disease during 1990-2019; they preferred to publish in Russian journals. The research contributions were in the form of research articles, meeting abstracts and reviews with a consistent drop in the number of editorial material and article; proceedings paper with time. Co-authorship was the norm in coronary artery disease research, with a steady increase in the number of multi-author documents in recent years.

Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.

Software is at the core of most scientific discoveries today. Therefore, the quality of research results highly depends on the quality of the research software. Rigorous testing, as we know it from software engineering in the industry, could ensure the quality of the research software but it also requires a substantial effort that is often not rewarded in academia. Therefore, this research explores the effects of research software testing integrated into teaching on research software. In an in-vivo experiment, we integrated the engineering of a test suite for a large-scale network simulation as group projects into a course on software testing at the Blekinge Institute of Technology, Sweden, and qualitatively measured the effects of this integration on the research software. We found that the research software benefited from the integration through substantially improved documentation and fewer hardware and software dependencies. However, this integration was effortful and although the student teams developed elegant and thoughtful test suites, no code by students went directly into the research software since we were not able to make the integration back into the research software