1,720,954 research outputs found
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
Improving Rare Disease Prediction with Specialized Loss Functions
No full textAutomated medical coding aims to assign standardized diagnosis and procedure codes to clinical documents, but faces significant challenges due to severe class imbalance between common and rare codes. This thesis investigates how specialized loss functions can improve rare disease prediction in the context of automated ICD-9 code assignment using the large language models (LLMs) with the MIMIC-III dataset. We implemented and evaluated two specialized loss functions Focal Loss (FL) and Asymmetric Loss (ASL) with the state-of-the-art PLM-ICD (Pre-trained Language Model for International Classification of Diseases) model architecture. These lossfunctions were designed to address class imbalance by differentially weighting the examples based on classification difficulty and positive/negative class separation. Our experiments show that ASL achieved the best performance, improving Macro F1 scores by 0.023 to the standard Binary Cross-Entropy loss while maintaining comparable Micro-F1 performance. Focal Loss also consistently improved rare disease prediction across multiple parameter configurations. Our findings confirm that loss function engineering is an effective approach for improving rare disease prediction in automated medical coding systems. This work contributes to addressing the performance gap between common and rare codes, potentially enhancing the clinical utility of automated coding systems in healthcare settings.Automatiserad medicinsk kodning syftar till att tilldela standardiserade diagnos och procedurkoder till kliniska dokument, men står inför betydande utmaningar på grund av allvarlig klassobalans mellan vanliga och sällsynta koder. Detta examensarbete undersöker hur specialiserade förlustfunktioner kan förbättra prediktion av sällsynta sjukdomar i samband med automatiserad ICD-9-kodning med hjälp avstora språkmodeller (LLMs) med MIMIC-III datasetet. Vi implementerade och utvärderade två specialiserade förlustfunktioner Focal Loss (FL) och Asymmetric Loss (ASL) och integrerade med den topp presterande PLM-ICD (Pre-trained Language Model for International Classification of Diseases) modellarkitekturen. Dessa förlustfunktioner utformades för att hantera klassobalans genom att differentiellt vikta exempel baserat på klassificeringssvårighet och positiv/negativ klasseparation. Våra experiment visar att ASL uppnådde bäst resultat, med en förbättring av Macro-F1 med 0.023 i förhållande till standard Binary Cross-Entropy loss, samtidigt som jämförbar Micro-F1-prestanda bibehölls. Focal Loss förbättrade också konsekvent prediktionen av sällsynta sjukdomar över flera parameterkonfigurationer. Våra resultat bekräftar att förlustfunktionsutveckling är en effektiv metod för att förbättra prediktion av sällsynta sjukdomar i automatiserade medicinska kodningssystem. Detta arbete bidrar till att minska prestationsgapet mellan vanliga och sällsynta koder, vilket potentiellt förbättrar den kliniska applikationen av automatiserade kodningssystem inom vårdmiljöer
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
Improving Rare Disease Prediction with Specialized Loss Functions
No full textAutomated medical coding aims to assign standardized diagnosis and procedure codes to clinical documents, but faces significant challenges due to severe class imbalance between common and rare codes. This thesis investigates how specialized loss functions can improve rare disease prediction in the context of automated ICD-9 code assignment using the large language models (LLMs) with the MIMIC-III dataset. We implemented and evaluated two specialized loss functions Focal Loss (FL) and Asymmetric Loss (ASL) with the state-of-the-art PLM-ICD (Pre-trained Language Model for International Classification of Diseases) model architecture. These lossfunctions were designed to address class imbalance by differentially weighting the examples based on classification difficulty and positive/negative class separation. Our experiments show that ASL achieved the best performance, improving Macro F1 scores by 0.023 to the standard Binary Cross-Entropy loss while maintaining comparable Micro-F1 performance. Focal Loss also consistently improved rare disease prediction across multiple parameter configurations. Our findings confirm that loss function engineering is an effective approach for improving rare disease prediction in automated medical coding systems. This work contributes to addressing the performance gap between common and rare codes, potentially enhancing the clinical utility of automated coding systems in healthcare settings.Automatiserad medicinsk kodning syftar till att tilldela standardiserade diagnos och procedurkoder till kliniska dokument, men står inför betydande utmaningar på grund av allvarlig klassobalans mellan vanliga och sällsynta koder. Detta examensarbete undersöker hur specialiserade förlustfunktioner kan förbättra prediktion av sällsynta sjukdomar i samband med automatiserad ICD-9-kodning med hjälp avstora språkmodeller (LLMs) med MIMIC-III datasetet. Vi implementerade och utvärderade två specialiserade förlustfunktioner Focal Loss (FL) och Asymmetric Loss (ASL) och integrerade med den topp presterande PLM-ICD (Pre-trained Language Model for International Classification of Diseases) modellarkitekturen. Dessa förlustfunktioner utformades för att hantera klassobalans genom att differentiellt vikta exempel baserat på klassificeringssvårighet och positiv/negativ klasseparation. Våra experiment visar att ASL uppnådde bäst resultat, med en förbättring av Macro-F1 med 0.023 i förhållande till standard Binary Cross-Entropy loss, samtidigt som jämförbar Micro-F1-prestanda bibehölls. Focal Loss förbättrade också konsekvent prediktionen av sällsynta sjukdomar över flera parameterkonfigurationer. Våra resultat bekräftar att förlustfunktionsutveckling är en effektiv metod för att förbättra prediktion av sällsynta sjukdomar i automatiserade medicinska kodningssystem. Detta arbete bidrar till att minska prestationsgapet mellan vanliga och sällsynta koder, vilket potentiellt förbättrar den kliniska applikationen av automatiserade kodningssystem inom vårdmiljöer
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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