1,721,002 research outputs found
Efficient large language models for the NHS and psychiatry
Full text linkElectronic Health Records (EHRs) contain large volumes of unstructured clinical text, produced for a large variety of reasons. The clinical notes within EHR can be full of technical jargon and have a low signal-to-noise ratio. This poses a challenge for healthcare providers like the UK National Health Service (NHS), as making use of these texts to produce insights can be labour-intensive and requires domain expertise. This thesis explores methods for developing efficient, cost-effective, be- spoke Large Language Models (LLMs) to understand NHS clinical text, enabling improved patient management and treatment. An enhanced pretraining regime, using contrastive loss on NHS clinical data, enabled the creation of NHS-specific LLMs within a day on a single GPU. These models outperformed open-source LLMs, facilitating faster adaptation to downstream clinical NLP tasks.
Traditional LLM fine-tuning is computationally expensive and challenging with larger models. Efficient adaptation methods, such as prompt learning, were devel- oped and employed, reducing computational and storage requirements by up to 98% while maintaining state-of-the-art performance on several clinical NLP tasks. The bespoke NHS LLMs and efficient adaptation methods were applied to a digital triage system for secondary mental health referrals. This system aimed to improve transparency, accuracy, and efficiency in routing patients to appropriate care pathways based on their clinical information. The resulting model processed variable-length patient referral text and produced triage team recommendations with an explainability tool to enhance interpretability. Crucially, the triage model remained cost-effective and feasible in resource-constrained environments. This work evaluates the feasibility, utility, and potential benefits of developing specialized LLMs for NHS clinical text processing, discussing implications for enhancing patient care and clinical decision support
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Saturation model in the non-Glauber approach
Full text linkIn this paper a new saturation model is presented. This model is based on the theoretical solution for the generating functional, and it is quite different and not more complicated than the Glauber-like approach used before. The model describes the structure function F2 of the proton, as well as the diffractive structure function FD2. We show the difference between our model and the eikonal approach by calculating the multiplicity distribution, using the AGK cutting rules strategy
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
A Primer on the Signature Method in Machine Learning
Full text linkIn these notes, we wish to provide an introduction to the signature method, focusing on its basic theoretical properties and recent numerical applications.The notes are split into two parts. The first part focuses on the definition and fundamental properties of the signature of a path, or the path signature. We have aimed for a minimalistic approach, assuming only familiarity with classical real analysis and integration theory, and supplementing theory with straightforward examples. We have chosen to focus in detail on the principle properties of the signature which we believe are fundamental to understanding its role in applications. We also present an informal discussion on some of its deeper properties and briefly mention the role of the signature in rough paths theory, which we hope could serve as a light introduction to rough paths for the interested reader.The second part of these notes discusses practical applications of the path signature to the area of machine learning. The signature approach represents a non-parametric way for extraction of characteristic features from data. The data are converted into a multi-dimensional path by means of various embedding algorithms and then processed for computation of individual terms of the signature which summarise certain information contained in the data. The signature thus transforms raw data into a set of features which are used in machine learning tasks. We will review current progress in applications of signatures to machine learning problems
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