1,720,966 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
Detección precoz de sepsis (se) y shock séptico (ss) utilizando técnicas de big data, inteligencia artificial y machine learning
Get PDF[cat] Objectius: L'objectiu d'aquest estudi va ser desenvolupar i validar models predictius per a la detecció de sepsi greu (SG) i xoc sèptic (SS) en pacients majors de 14 anys d'un Hospital Universitari, emprant metodologies avançades de Big Data (BD), Intel·ligència Artificial (IA) i Aprenentatge Automàtic (Machine Learning, ML), i comparar la seva efectivitat amb altres mètodes diagnòstics tradicionals. Per tant, vam dissenyar un estudi per perfeccionar la capacitat predictiva de diferents models per diferenciar efectivament entre pacients amb i sense SG/SS, minimitzant així els falsos positius i negatius.
Disseny Metodològic: Es va dur a terme una anàlisi retrospectiva de pacients prèviament identificats i validats de manera prospectiva com a casos de SG/SS pels especialistes de la Unitat Multidisciplinària de Sepsis (UMS). Aquesta anàlisi va utilitzar diverses fonts de dades de la Història Clínica Electrònica (HCE), tant estructurades com no estructurades (incloent text lliure i Processament de Llenguatge Natural, PLN), per construir models predictius. Les variables incloïen dades demogràfiques, signes vitals i clínics, resultats de laboratori, prescripcions farmacèutiques, informes microbiològics, informació de triatge i resums d'alta d'Urgències.
Àmbit d'Estudi: L'estudi es va centrar en totes les àrees d'hospitalització, Urgències i d'UCI de l'Hospital Universitari Son Llàtzer, a Palma de Mallorca, Espanya.
Pacients: Tots els pacients majors de 14 anys inclosos en el període d'estudi.
Període d'Estudi: L'anàlisi va abastar el període des de l'1 de gener de 2014 fins al 31 de desembre de 2018.
Resultats: Es van examinar 815.170 registres de la HCE, corresponents a 461.392 episodis de 203.755 pacients, dividits en dos grups: aquells amb SG/SS (4,56%) i els sense sepsi (95,44%). De 2.829 variables identificades, 229 (8,09%) van mostrar una correlació significativa amb la detecció de SG/SS, validades per l'equip de la Unitat de Monitorització de Sepsis (UMS). Es va observar una variabilitat notable en l'associació de variables amb SG/SS segons el Servei hospitalari. Els models predictius basats en ML van exhibir una capacitat sobresortint per detectar SG/SS, sent el millor model una combinació (ensemble) entre ML més el criteri de SEPSIS.2 que va assolir un AUC-ROC de 0,95, amb sensibilitat i especificitat de 0,93 i 0,84, respectivament.
Conclusió: L'aplicació de models predictius avançats basats en IA-ML ha resultat en eines més adequades, dinàmiques i personalitzades per a la detecció de SG/SS en pacients de totes les àrees d'un hospital que escores convencionals.[spa] Objetivos: El propósito de este estudio fue desarrollar y validar modelos predictivos para la detección de sepsis grave (SG) y shock séptico (SS) en pacientes mayores de 14 años de un Hospital Universitario, empleando metodologías avanzadas de Big Data (BD), Inteligencia Artificial (IA) y Aprendizaje Automático (Machine Learning, ML), y comparar su efectividad con otros métodos diagnósticos tradicionales. Por lo que diseñamos un estudio para perfeccionar la capacidad predictiva de diferentes modelos para diferenciar efectivamente entre pacientes con y sin SG/SS, minimizando así los falsos positivos y negativos.
Diseño Metodológico: Se llevó a cabo un análisis retrospectivo de pacientes previamente identificados y validados de forma prospectiva como casos de SG/SS por especialistas de la Unidad Multidisciplinar de Sepsis (UMS). Este análisis utilizó diversas fuentes de datos de la Historia Clínica Electrónica (HCE), tanto estructurados como no estructurados (incluyendo texto libre y Procesamiento de Lenguaje Natural, PLN), para construir modelos predictivos. Las variables incluyeron datos demográficos, signos vitales y clínicos, resultados de laboratorio, prescripciones farmacéuticas, informes microbiológicos, información de triaje y resúmenes de alta de Urgencias.
Ámbito de Estudio: El estudio se centró en todas las áreas de hospitalización, Urgencias y de UCI del Hospital Universitario Son Llàtzer, en Palma de Mallorca, España.
Pacientes: Todos los pacientes mayores de 14 años incluidos en el periodo del estudio.
Periodo de Estudio: El análisis comprendió el periodo desde el 1 de enero de 2014 hasta el 31 de diciembre de 2018.
Resultados: Se examinaron 815,170 registros de la HCE, correspondientes a 461,392 episodios de 203,755 pacientes, divididos en dos grupos: aquellos con SG/SS (4,56%) y los sin sepsis (95,44%). De 2,829 variables identificadas, 229 (8,09%) mostraron una correlación significativa con la detección de SG/SS, validadas por el equipo de la Unidad de Monitorización de Sepsis (UMS). Se observó una variabilidad notable en la asociación de variables con SG/SS según el Servicio hospitalaria. Los modelos predictivos basados en ML exhibieron una capacidad sobresaliente para detectar SG/SS, siendo el mejor modelo una combinación (ensemble) entre ML más el criterio de SEPSIS.2 que alcanzó un AUC-ROC de 0,95, con sensibilidad y especificidad de 0,93 y 0,84, respectivamente.
Conclusión: La aplicación de modelos predictivos avanzados basados en IA-ML ha resultado en herramientas más adecuadas, dinámicas y personalizadas para la detección de SG/SS en pacientes de todas las áreas de un hospital que escores convencionales.[eng] Objectives: The aim of this study was to develop and validate predictive models for the detection of severe sepsis (SG) and septic shock (SS) in patients over the age of 14 at a University Hospital, utilizing advanced Big Data (BD), Artificial Intelligence (AI), and Machine Learning (ML) methodologies, and to compare their effectiveness with traditional diagnostic methods. We designed a study to refine the predictive ability of various models to effectively distinguish between patients with and without SG/SS, thus minimizing false positives and negatives.
Methodological Design: A retrospective analysis was conducted on patients previously identified and prospectively validated as SG/SS cases by specialists from the Multidisciplinary Sepsis Unit (UMS). This analysis used a variety of data sources from the Electronic Health Record (EHR), both structured and unstructured (including free text and Natural Language Processing, NLP), to construct predictive models. Variables included demographic data, vital and clinical signs, laboratory results, pharmaceutical prescriptions, microbiological reports, triage information, and Urgent Care discharge summaries.
Scope of Study: The research encompassed all hospitalization areas, Emergency and ICU departments of the University Hospital Son Llàtzer in Palma de Mallorca, Spain.
Patients: All patients over the age of 14 included in the study period.
Study Period: The analysis spanned from January 1, 2014, to December 31, 2018.
Results: A total of 815,170 EHR records were examined, corresponding to 461,392 episodes from 203,755 patients, divided into two groups: those with SG/SS (4.56%) and those without sepsis (95.44%). Out of 2,829 identified variables, 229 (8.09%) demonstrated a significant correlation with SG/SS detection, validated by the UMS team. Notable variability was observed in the association of variables with SG/SS depending on the hospital service. The ML-based predictive models exhibited outstanding capability in detecting SG/SS, with the best model being a combination (ensemble) of ML plus the SEPSIS.2 criteria, achieving an AUC-ROC of 0.95, with sensitivity and specificity of 0.93 and 0.84, respectively.
Conclusion: The application of advanced predictive models based on AI-ML has yielded more suitable, dynamic, and personalized tools for detecting SG/SS in patients across all hospital areas compared to conventional scores
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
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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