1,721,115 research outputs found
A synthesis of hydroclimatic, ecological, and socioeconomic data for transdisciplinary research in the Mekong
No full textVarious climate, hydro-meteorological, ecological, and socio-economic datasets are synthesized and made available for the Mekong River Basin. The sources of each dataset are also mentioned in the associated readme file.
Dam attribute data, inundation data, and Cambodia census data can be made available upon request to the authors
Leveraging Knowledge and Data-Driven Approaches in Case-Based Reasoning to Aid Physiotherapists Addressing Non-Specific Musculoskeletal Disorders
Get PDFBACKGROUND: Non-specific musculoskeletal disorders (MSDs) remain one of the most prevalent and challenging conditions in contemporary healthcare, affecting a significant proportion of the population and leading to substantial socioeconomic burdens. The complexity of MSDs arises from their heterogeneous symptomatology, uncertain etiology, and the absence of definitive diagnostic biomarkers. These factors make diagnosis and treatment highly dependent on subjective clinical assessments, often resulting in inconsistencies in treatment outcomes. Traditional physiotherapy approaches, which follow generalized treatment guidelines, struggle to account for individual differences in patient conditions, functional impairments, and response to therapy. The variability in symptoms and the subjective nature of clinical assessments contribute to the lack of standardized decision-making processes, leading to challenges in ensuring optimal patient outcomes. Recent advances in artificial intelligence (AI), particularly in case-based reasoning (CBR), present new opportunities to improve clinical decision-making in physiotherapy. Using systematically the knowledge of previous patient cases, CBR-driven clinical decision support systems (CDSS) offer a data-driven approach that enhances the personalization and effectiveness of treatment recommendations. However, several challenges remain, including the development of robust and interpretable patient similarity assessment models, the establishment of fair and unbiased retrieval evaluation metrics, and the design of scalable AI frameworks that comply with privacy regulations such as the General Data Protection Regulation (GDPR). These challenges must be addressed to ensure the practical deployment of AI-driven CDSS in musculoskeletal healthcare and to enable evidence-based decision support that aligns with clinical workflows.
OBJECTIVE: The primary objective of this research is to develop a CBR-driven CDSS that integrates both knowledge-based and data-driven methodologies to improve decision-making in physiotherapy for non-specific MSDs. The study aims to establish structured computational models for the assessment of patient similarity, ensuring that the retrieval of the case is clinically relevant and interpretable. In addition, it focuses on designing and validating fair and normalized retrieval evaluation metrics that enhance transparency, reliability, and fairness in learning retrieval systems. Another key objective is to develop a federated CBR framework that enables privacy-preserving knowledge sharing across multiple institutions while ensuring compliance with data security regulations. The final goal is to evaluate the effectiveness and clinical usability of the proposed system through empirical validation in real-world physiotherapy settings, evaluating its ability to improve clinical workflows and improve patient outcomes.
METHODS: This research follows an applied interdisciplinary approach that integrates artificial intelligence, knowledge engineering, information retrieval, and clinical validation. A structured case representation model was developed using real-world datasets from the FYSIOPRIM and SupportPrim studies of Norway to enable efficient retrieval of similar patients from a CBR system. This model ensures that patient similarity assessments remain viable even in knowledge-light domains while optimizing case-based decision-making. A fair and normalized retrieval evaluation metric (FaN-REM) was proposed to address biases in traditional evaluation methods by incorporating systemgenerated and oracle-assessed retrieval relevancy scores, ensuring fair and unbiased retrieval ranking. Robust and scalable Supportprim CBR and CDSS systems were developed based on microservices framework to be deployable in a real-world primary care clinical setting. These CBR and CDSS systems were empirically evaluated through experimental studies and a randomized controlled trial (RCT) in Norwegian primary care settings, where their usability, clinical relevance and impact on the physiotherapy workflows were evaluated. Furthermore, a decentralized and privacy-preserving federated CBR architecture was developed to enable GDPR-compliant multi-institutional collaboration without requiring centralized data sharing. The federated approach ensures that institutions maintain control over patient data while benefiting from collective knowledge sharing.
RESULTS: The proposed CBR-driven CDSS demonstrated several key advancements in AI-assisted physiotherapy decision support. The system achieved improved retrieval accuracy compared to traditional knowledge-based approaches, ensuring that physiotherapists have access to more relevant patient cases for decision support. The introduction of the FaN-REM framework enabled unbiased retrieval evaluations, improving the fairness and transparency of a learning retrieval system such as the CBR system, thus reinforcing clinician trust in the system. The federated CBR architecture successfully facilitated secure knowledge sharing across multiple institutions while ensuring compliance with data protection regulations, enabling collaborative learning that preserves privacy. These findings confirm that AI-driven decision support, particularly through CBR methodologies, can effectively bridge the gap between clinical intuition and data-driven insights, leading to more standardized and evidencebased treatment pathways for non-specific MSDs.
CONCLUSION: This research establishes CBR as a transformative AI methodology to address the complexities of non-specific MSDs. By integrating structured case representation models, fair retrieval evaluation frameworks, and federated learning techniques, the study provides a scalable, privacy-compliant, and clinically viable AI-driven CDSS. The methodologies proposed in this research are applicable not only to musculoskeletal healthcare, but also extend to broader AI-driven decision support applications in medicine, demonstrating the potential of AI to improve personalized treatment recommendations in various domains. The CBR and CDSS systems developed under this research were successfully deployed in real-world primary care settings in Norway. The insights derived from this research lay the foundation for the next generation of AI-assisted clinical decision support systems, ensuring that future healthcare solutions are data-driven, scalable, and ethically responsible. By addressing challenges related to fairness, privacy, and clinical usability, this research paves the way for AI to become an integral component of evidence-based decisionmaking in physiotherapy and beyond
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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